# Awesome Conformal Prediction | Massive Resources & Collection ⭐

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## Awesome Conformal Prediction | Massive Resources & Collection ⭐

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles, and open-source libraries.

Explore the most extensive professionally curated collection on Conformal Prediction, featuring top-notch tutorials, videos, books, papers, articles, courses, websites, conferences, and open-source libraries in Python, R, and Julia. Uncover the hidden gems and master the art of Conformal Prediction with this all-encompassing guide. Experience the Pinnacle of Conformal Prediction Expertise: A Resource Crafted by a Pro.

## Table of Contents

- Events
- Books
- PhD and MSc Theses
- Videos
- Papers
- Papers Time Series
- Articles
- Kaggle
- Tutorials
- Courses
- Presentation\_slides
- Researchers
- Websites
- Twitter
- TikTok
- Conferences
- Python
- R
- Julia
- Other Languages
- AI platforms

## Events

1. Kaggle competition [“Prediction interval competition I: Birth weight.”](https://www.kaggle.com/competitions/prediction-interval-competition-i-birth-weight/discussion/459133) an opportunity to learn about and practice conformal prediction 🔥

## Books

1. [Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications](https://a.co/d/9guwCTm) by Valeriy Manokhin (2024) 🔥 [**Amazon USA 🇺🇸**](https://www.amazon.com/gp/aw/d/1805122762), [**Amazon UK 🇬🇧**](https://www.amazon.co.uk/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon India 🇮🇳**](https://www.amazon.in/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS), [**Amazon Germany 🇩🇪**](https://www.amazon.de/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/), [**Amazon France 🇫🇷**](https://www.amazon.fr/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Spain 🇪🇸**](https://www.amazon.es/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon Canada 🇨🇦**](https://www.amazon.ca/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Japan 🇯🇵**](https://www.amazon.co.jp/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/) 🔥🚀
2. [Algorithmic Learning in a Random World](https://link.springer.com/book/10.1007/978-3-031-06649-8) by Vladimir Vovk and Alex Gammerman, also Glenn Shafer (2022). Second edition. 🔥 great theory based book, very math heavy, no applications, no code
3. [Conformal Prediction for Reliable Machine Learning](https://www.elsevier.com/books/conformal-prediction-for-reliable-machine-learning/balasubramanian/978-0-12-398537-8) by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk (2014) OLD BOOK, largely out of date, no code

## Theses

1. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/361515440_Machine_Learning_for_Probabilistic_Prediction_PhD_thesis_VALERY_MANOKHIN), PhD Thesis, Valery Manokhin (Royal Holloway, UK, 2022) 🔥 (over 6K reads on Researchgate alone)
2. [Conformal and Venn Predictors for large, imbalanced and sparse chemoinformatics data](https://pure.royalholloway.ac.uk/portal/files/41316291/2021toccacelipphd.pdf), PhD Thesis, Paolo Toccaceli (Royal Holloway, UK, 2021)
3. [Competitive online algorithms for probabilistic prediction](https://pure.royalholloway.ac.uk/portal/files/36216771/Thesis_Raisa.pdf), PhD Thesis, Raisa Dzhamtyrova (Royal Holloway, UK, 2020)
4. [Conformal Prediction and Testing under On-line Compression Models](https://pure.royalholloway.ac.uk/portal/files/20318074/2014fedorovavphd.pdf), PhD Thesis, Valentina Fedorova (Royal Holloway, UK, 2014) 🔥
5. [Adaptive Online Learning](https://pure.royalholloway.ac.uk/portal/files/17462972/adamskiy_thesis.pdf), PhD Thesis, Dmitry Adamskiy (Royal Holloway, UK, 2013)
6. [On discovery and exploitation of temporal structure in data sets](https://pure.royalholloway.ac.uk/portal/files/24367944/Thesis.pdf), PhD Thesis, Tim Scarfe, (Royal Holloway, UK, 2015)
7. [Black-box Security Measuring Black-box Information Leakage via Machine Learning](https://pure.royalholloway.ac.uk/portal/files/33806285/thesis_final_after_amendments.pdf), PhD Thesis, Giovanni Cherubin (Royal Holloway, UK, 2019) 🔥
8. [Small and Large Scale Probabilistic Classifiers with Guarantees of Validity](https://pure.royalholloway.ac.uk/portal/files/30400650/2018PetejIPhd.pdf), PhD Thesis, Ivan Petej (Royal Holloway, UK, 2019) 🔥
9. [Confidence and Venn Machines and Their Applications to Proteomics](https://pure.royalholloway.ac.uk/portal/files/1402925/PhD_Thesis_Final_Dmitry_Devetyarov.pdf) by Devetyarov, Dmitry (Royal Holloway, UK, 2019)
10. [Conformal Anomaly Detection - detecting abnormal trajectories in surveillance applications](https://www.diva-portal.org/smash/get/diva2:690997/FULLTEXT02.pdf) by Rikard Laxhammar (University of Skoeve, Sweden, 2014) 🔥
11. [Inductive Confidence Machine for Pattern Recognition - is it the next step towards AI](https://github.com/eghamtech/AIOS/blob/master/notebooks/David_Surkov-RHUL-thesis-2004.pdf) by David Surkov (Royal Holloway, UK, 2004)
12. [Distribution Free Prediction Intervals for Multiple Functional Regression](http://d-scholarship.pitt.edu/39495/1/RMK%20Dissertation%20Final.pdf) by Ryan Kelly (University of Pittsburgh, 2020).
13. [Probabilistic Load Forecasting with Deep Conformalized Quantile Regression](https://munin.uit.no/bitstream/handle/10037/21914/thesis.pdf?sequence=2) by Vilde Jensen (Artcic University of Norway, 2021)
14. [Model-free methods for multiple testing and predictive inference](https://searchworks.stanford.edu/view/13876512), PhD Thesis, Zhimei Ren (Stanford, 2021) 🔥
15. [Comparison of Support Vector Machines and Deep Learning For QSAR with Conformal Prediction](https://www.diva-portal.org/smash/get/diva2:1682247/FULLTEXT01.pdf) by Deligianni Maria, MSc thesis, Universit of Uppsala (2022)
16. [Predictive Maintenance with Conformal and Probabilistic Prediction: A Commercial Case Study](https://www.jamesgammerman.com/files/Thesis.pdf) by James Gammerman (2022)
17. [Risk-Sensitive Decision-Making for Autonomous-Driving](https://www.diva-portal.org/smash/get/diva2:1698692/FULLTEXT01.pdf) by Hardy Hasan (University of Uppsala, 2022)
18. [Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction](https://arxiv.org/pdf/2210.14735.pdf), MSc thesis by Roel Hulsman, University of Oxford (2022)
19. [Coreset-based Protocols for Machine Learning Classification](https://pure.royalholloway.ac.uk/en/publications/coreset-based-protocols-for-machine-learning-classification), PhD thesis by Nery Riquelme Granada (Royal Holloway, University of London, 2022)
20. [Conformal survival predictions at a user-controlled time point](https://kth.diva-portal.org/smash/get/diva2:1231989/FULLTEXT01.pdf) by Jelle Van Miltenburg (KTH ROYAL INSTITUTE OF TECHNOLOGY, 2018)
21. [Reliable Machine Learning with ConformalPrediction: A Review with Contributions](https://www.researchgate.net/publication/366205428_Reliable_Machine_Learning_with_Conformal_Prediction_A_Review_with_Contributions) by Martim Sousa (2022) 🔥
22. [Nonconformity Measures and Ensemble Strategies - An Analysis of Conformal Predictor Efficiency and Validity](http://su.diva-portal.org/smash/get/diva2:1547120/FULLTEXT01.pdf), PhD thesis by Henrik Linusson (Stockholm University, 2021) 🔥
23. [Determine and explain confidence in predicting violations on inland ships in the Netherlands](https://repository.tudelft.nl/islandora/object/uuid:e0a9bb58-98ab-4c00-96ae-8701046a8012) by Bakker, Paul (TU Delft, 2020)
24. [Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0](https://www.diva-portal.org/smash/get/diva2:1765779/FULLTEXT01.pdf) by Shuzhou Liu, Mulahuko Mpova (Jönköping University, 2023).
25. [Benchmarking conformal prediction methods for time series regression](https://github.com/valeman/awesome-conformal-prediction/blob/main/BScThesis_DerckPrinzhorn.pdf) by Derck W.E. Prinzhorn (2023) [code](https://github.com/dweprinz/Benchmarking-conformal-prediction-methods-for-time-series-regression) 🔥
26. [Conformal Prediction Methods in Finance](https://impa.br/wp-content/uploads/2022/11/Projeto_Final_Joao-Vitor-Romano.pdf) by Finance João Vitor Romano (Instituto de Matemática Pura e Aplicada, Brazil, 2022) 🔥
27. [An Active Learning Approach for Reject Inference in Credit Scoring using Conformal Prediction Intervals on Real and Semi-Artificial Data](https://github.com/MaximilianSuliga/Conformal-Active-Learning-for-Reject-Inference) by Maximilian Suliga (Humboldt University of Berlin, 2023)
28. [Confidence Predictions in Pharmaceutical Sciences](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1787351&dswid=-7234) by Staffan Arvidsson McShane (Uppsala University, Sweden, 2023) 🔥
29. [Conform With the Wind Processing short-term ensemble forecasts with conformal based methods for probabilistic wind-speed forecasting](https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9130213&fileOId=9130294), MSc thesis by Simon Althoff (Lund University, 2023)
30. [Trustworthy explanations: Improved decision support through well-calibrated uncertainty quantification](https://hj.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=4&af=%5B%5D&searchType=SIMPLE&sortOrder2=title_sort_asc&query=Helena+L%C3%B6fstr%C3%B6m&language=en&pid=diva2%3A1810440&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-3088), PhD thesis by Helena Löfström (Jönköping Universitu, 2023) 🔥
31. [Data Augmentation and Conformal Prediction](https://dspace.mit.edu/handle/1721.1/151275), MSc thesis by Helen Lu (MIT, 2023)
32. [Conformal prediction and copula based methods for profile monitioring](https://www.politesi.polimi.it/handle/10589/214268), MSc Thesis by Niccolo’ Donadini (Politecnico di Milano, 2023)

## Tutorials

1. [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://arxiv.org/pdf/2107.07511.pdf) by Anastasios N. Angelopoulos and Stephen Bates (2021) [Video](https://www.youtube.com/watch?v=nql000Lu_iE&t=1786s) [Code](https://github.com/aangelopoulos/conformal-prediction) 🔥
2. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (2020) 🔥
3. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) 🔥
4. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020) 🔥
5. [Predicting with Confidence - Henrik Boström](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Boström (2016) 🔥
6. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sönströd, Tuve Löfström, and Henrik Boström (2021) 🔥
7. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (2020) 🔥
8. [A Tutorial on Conformal Prediction](https://jmlr.org/papers/v9/shafer08a.html) by Glenn Shafer and Vladimir Vovk (2008) 🔥
9. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana1 and Simone Vantini (Politecnico di Milano, Italy, 2021) 🔥
10. [Conformal Prediction in Spark](https://docs.google.com/presentation/d/1eD1vUJVR3nejyJZsOMfxL-NxrLy7K2UUMSAEMHYLfek/edit#slide=id.g2276269343_0_129) by Marco Capuccini (Uppsala University, 2017)
11. [Tutorial on Venn-ABERS prediction](https://cml.rhul.ac.uk/people/ptocca/HomePage/Toccaceli_CP___Venn_Tutorial.pdf) by Paolo Toccaceli (Royal Holloway, UK, 2019) 🔥
12. [An Introduction to Conformal Prediction](https://cml.rhul.ac.uk/copa2017/presentations/CP_Tutorial_2017.pdf) by Henrik Linusson (2017) 🔥
13. [Introduction to Conformal Prediction](https://www.iith.ac.in/~vineethnb/indoukworkshop2015/assets/files/IntroToCP.pdf) by Vineeth N Balasubramanian (Indian Institue of Technology, Hyderabad, 2015)
14. [Conformal prediction A Tiny Tutorial on Predicting with Confidence](https://people.dsv.su.se/~henke/DSWS/johansson.pdf) by Henrik Linusson and Ulf Johansson (2014)
15. [Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://www.youtube.com/watch?v=usaHyuu2TzY) by Anastasios Angelopoulos (Berkeley, 2022)
16. [A Tutorial on Conformal Prediction Part 2: Conditional Coverage and Diagnostics](https://www.youtube.com/watch?v=TRx4a2u-j7M&t=529s) by Anastasios Angelopoulos and Stephen Bates (2022) 🔥
17. [Beyond Conformal Prediction: Tutorial on Conformal Part 3](https://www.youtube.com/watch?v=37HKrmA5gJE) by Anastasios Angelopoulos and Stephen Bates (2022) 🔥
18. [Getting predictions intervals with conformal inference](http://projects.rajivshah.com/blog/2022/09/24/conformal_predictions) by Rajiv Shah (2022) [YouTube](https://www.youtube.com/watch?v=ZUK4zR0IeLU) [Code](https://colab.research.google.com/drive/1bA_TrrmRpgJ0jasWBZCxkXSLePi8uWBx) 🔥
19. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, USC, 2022) 🔥
20. [Regression prediction intervals with MAPIE on Kaggle](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie) by Carl McBride Ellis, PhD
21. [Distribution-free inference tutorial At the IFDS 2021 Summer School](https://rinafb.github.io/talks/) [Video 1](https://vimeo.com/581870072) [Video 2](https://vimeo.com/581870078)
22. [Conformal Inference Tutorial](https://bkompa.github.io/2020/09/19/Conformal-Inference-Tutorial.html) by Ben Kompa (2020)
23. [Prediction intervals for any machine learning model - How to construct prediction intervals with the Jackknife+ using the MAPIE package](https://www.valencekjell.com/posts/2022-09-14-prediction-intervals/) by Kjell Jorner (ETH, 2022)
24. [Uncertainty Quantification (1): Enter Conformal Predictors](https://www.youtube.com/watch?v=xZbuFKWV5NA&t=25s) by Mahdi Torabi Rad (2023) 🔥
25. [Uncertainty Quantification (2): Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) 🔥
26. [Uncertainty Quantification (3): From Full to Split Conformal Methods](https://www.youtube.com/watch?v=YigGJfsCjDk) by Mahdi Torabi Rad (2023) 🔥
27. [Conformal Prediction and Venn Predictors A Tutorial on Predicting with Confidence](https://icdatascience.org/wp-content/uploads/2019/07/ICDATA_tutorial_2019_Johansson_U.pdf) by Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström, Alex Gammerman (2019) 🔥
28. [Tutorial on Conformal Prediction Claire Boyer Assistant Professor, LPSM, Paris Sorbonne Université ; Margaux Zaffran PhD Candidate, EDF, Inria, CMAP, École Polytechnique](https://claireboyer.github.io/tutorial-conformal-prediction/) 🔥
29. [Conformal Prediction: How to quantify uncertainty of machine learning models? ECAS-ENBIS course– ENBIS 2023 Annual conference](https://mzaffran.github.io/assets/files/Talks/Tuto_CP_ENBIS_ECAS.pdf) by Margaux Zaffran 🔥 (2023)
30. [Conformal Prediction in Genomics](https://github.com/BiolApps/ConformalPrediction/tree/main) by BiolApps (2023)
31. [Conformal Prediction: A Visual Introduction](https://conformal-prediction.streamlit.app/) in [VISxAI](https://visxai.io/) by Mihir Agarwal, Lalit Chandra Routhu, Zeel B Patel and Nipun Batra (IIT Gandhinagar, IIT Patna, 2023)
32. [A Conformal Prediction tutorial, an introductive review of the basics](https://conformalpredictionintro.github.io/) by Margaux Zaffran (2023)
33. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) 🔥

## Courses

1. [Uncertain: Modern topics in uncertainty estimation](https://uncertaintyclass.com/) [YouTube](https://www.youtube.com/watch?app=desktop&v=M3tkM4dcIPA) [Course notes](https://www.cis.upenn.edu/~aaroth/uncertainty-notes.pdf) by Aaron Roth (University of Pennsylvania, 2022) 🔥
2. [Topics in Modern Statistical Learning](https://github.com/dobriban/Topics-In-Modern-Statistical-Learning) by Edgar Dobriban (Wharton School, University of Pennsylvania, 2022) 🔥
3. [Theory of Statistics](https://candes.su.domains/teaching/stats300c/) Stanford Statistics course by Prof Emmanuel Candes (2022) 🔥
4. [36-708 Statistical Methods for Machine Learning](https://www.stat.cmu.edu/~larry/=sml/) Carnegie-Mellon course by Prof Larry Wasserman (2022)
5. [Course on Conformal Prediction](https://mindfulmodeler.substack.com/p/e-mail-course-on-conformal-prediction) by Christoph Molnar (2022)
6. [Conformal Prediction - Advanced Topics in Statistical Learning, Spring 2023](https://www.stat.berkeley.edu/~ryantibs/statlearn-s23/lectures/conformal.pdf) by Ryan Tibshirani (2023) 🔥
7. [Conformal Methods for Efficient and Reliable Deep Learning](https://dspace.mit.edu/handle/1721.1/152788) by Adam, Fisch (MIT, 2023) 🔥
8. [Conformal Prediction 101 - in Portuguese 🇵🇹](https://github.com/gusbruschi13/Conformal-Prediction/tree/main/cp-101) by Gustavo Bruschi (2023) 🔥

## Videos

1. [Treatment of Uncertainty in the Foundations of Probability](https://www.youtube.com/watch?v=B7E-QJ9fm4w&t=2297s) by Vladimir Vovk (Royal Holloway, UK, 2017)
2. [Large-Scale Probabilistic Prediction With and Without Validity Guarantees](https://www.youtube.com/watch?v=ksrUJdb2tA8) by Vladimir Vovk (Royal Holloway, UK, NeurIPS 2015) 🔥
3. [Conformal testing in a binary model situation](https://www.youtube.com/watch?v=RTcT4YXRdMg) by Vladimir Vovk (Royal Holloway, UK, 2021)
4. [Protected probabilistic classification](https://www.youtube.com/watch?v=MpP-3suUoLY) by Vladimir Vovk (Royal Holloway, UK, 2021)
5. [Retrain or not retrain: conformal test martingales for change-point detection](https://www.youtube.com/watch?v=4Ra5KnDEfkw&t=1s) by Vladimir Vovk (Royal Holloway, UK, 2021) 🔥
6. [A Tutorial on Conformal Prediction](https://www.youtube.com/watch?v=nql000Lu_iE&t=1786s) by Anastasios Angelopoulos and Stephen Bates (Berkeley, ICML 2021) 🔥
7. [Steps Toward Trustworthy Machine Learning](https://www.youtube.com/watch?v=2iNRSgS7-L4) by Tom Dietterich (2021)
8. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (Royal Holloway, UK, 2020) 🔥
9. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) 🔥
10. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020)
11. [Predicting with Confidence - Henrik Boström](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Boström (2016)
12. [How to increase certainty in predictive modeling](https://www.youtube.com/watch?v=fQQP84yxCRs&t=1277s) by Emmanuel Candes (Stanford, 2021) 🔥
13. [Recent Progress in Predictive Inference](https://www.youtube.com/watch?v=tY73G_UvkAE&t=833s) by Emmanuel Candes (Stanford, 2020) 🔥
14. [Some recent progress in predictive inference" (Stanford) @ MAD+](https://www.youtube.com/watch?v=djgxwpJQyAA) by Emmanuel Candes (Stanford, 2020)
15. [Conformal Prediction in 2020](https://www.youtube.com/watch?v=61tpigfLHso&t=1507s) by Emmanuel Candes (Stanford, 2020) 🔥
16. [Assumption-free prediction intervals for black-box regression algorithms](https://www.youtube.com/watch?v=GMnCO7_HIOY&t=3943s) by Aaditya Ramdas (Carnegie Mellon, 2020) 🔥
17. [Maria Navarro: Quantifying uncertainty in Machine Learning predictions | PyData London 2019](https://www.youtube.com/watch?v=r6bhm_A-YcQ&t=12s) by Maria Navarro (2019)
18. [Conformal Prediction: Enhanced Method for Understanding the Prediction Quality](https://www.youtube.com/watch?v=_ZVuEWEfwuw&t=948s) by Artem Ryasik and Greg Landrum
19. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, and Henrik Boström (2021)
20. [Mondrian conformal predictive distributions](https://www.youtube.com/watch?v=dHNZxw8WQrs) by Henrik Boström, Ulf Johansson, and Tuwe Löfström (2021) 🔥
21. [Calibrating Multi-Class Models](https://www.youtube.com/watch?v=uCnFgTjUYto) by Ulf Johansson, Tuwe Löfström, and Henrik Boström (2021)
22. [Conformal testing in a binary model situation](https://www.youtube.com/watch?v=RTcT4YXRdMg) by Vladimir Vovk (Royal Holloway, UK, 2021)
23. [Conformal prediction in Orange](https://www.youtube.com/watch?v=qI1jOEour1g&t=14s) by Tomaž Hočevar and Blaž Zupan (2021)
24. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=ITJAR3fcNuI) by Anastasios Angelopoulos (Stanford, 2021) 🔥
25. [Conformal Prediction and Distribution-Free Calibration](https://synapse.math.univ-toulouse.fr/s/KDcWmmU9j9zk0rm) by Aaditya Ramdas (Carnegie Mellon, 2021) 🔥
26. [Reliable Diagnostics by Conformal Predictors](https://www.youtube.com/watch?v=zW3R-vbLw58) by Alexander Gammerman (Royal Holloway, UK, 2015)
27. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=ITJAR3fcNuI) by Anastasios Angelopoulos (Stanford, 2021) 🔥
28. [Conformal Inference of Counterfactuals and Time-to-event Outcomes](https://www.youtube.com/watch?v=nfD3mrSefbI) by Lihua Lei (Stanford, 2021)
29. [Algo Hour – Conformal Inference of Counterfactuals and Individual Treatment Effect](https://www.youtube.com/watch?v=COW2QNBmEMw) by Lihua Lei (Stanford, 2021)
30. [Conformal Inference of Counterfactuals and Individual Treatment effects(Stanford)](https://www.youtube.com/watch?v=8tM4BhONHms) by Lihua Lei (Stanford, 2021)
31. [Approximation to object conditional validity with inductive conformal predictors](https://www.youtube.com/watch?v=pUf7z2vxdi8) by Anthony Bellotti (University of Nottingham Ningbo, China, 2021)
32. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=40s) by Ulf Johansson (Jönköping University, Sweden, 2021) 🔥
33. [Transformer-based conformal predictors for paraphrase detection](https://www.youtube.com/watch?v=HYP1ypxywWo) by Patrizio Giavannotti and Prof. Alexander Gammerman (Royal Holloway, UK, 2021)
34. [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://www.youtube.com/watch?v=jkFs6pLZXBQ) by Lihua Lei (Stanford, 2020)
35. [Model-Free Predictive Inference](https://www.youtube.com/watch?v=8GkhRuWcd0w) by Larry Wasserman (Carnegie Mellon, 2020) 🔥
36. [Shapley-value based inductive conformal prediction](https://www.youtube.com/watch?v=6XUc3HFa_5Q&t=1094s) by William Lopez Jaramillo (2021)
37. [Conformal Training: Learning Optimal Conformal Classifiers | DeepMind](https://www.youtube.com/watch?v=XMa1glDpVtQ&t=318s) by David Stutz (2021) 🔥
38. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=z8WDmD5D-I0) by Anastasios Angelopoulos (2021) 🔥
39. [Assumption-Free, High-Dimensional Inference](https://www.youtube.com/watch?v=UOMvUaYfpH4) by Larry Wasserman (2016)
40. [Neural Predictive Monitoring under Partial Observability](https://www.youtube.com/watch?v=JhJXUDPoKCc) by Francesca Cairolli (2021)
41. [Conformalized Kernel Ridge Regression and Its Efficiency](https://www.youtube.com/watch?v=OLeu9TXE5n4) by Evgeny Burnaev (Skolkovo, Russia, 2015)
42. [Fast conformal classification using influence functions](https://www.youtube.com/watch?v=LRwm976poDE) by Giovanni Cherubin (Alan Turing Institute, UK, 2021)
43. [Valid inferential models and conformal prediction](https://www.youtube.com/watch?v=egrLw0CmXTs) by Ryan Martin (North Carolina State University, USA, 2021)
44. [Mondrian conformal predictive distributions](https://www.youtube.com/watch?v=dHNZxw8WQrs&t=1415s) by Henrik Boström, Ulf Johansson and Tuwe Löfström (KTH Royal Institute of Technology, Sweden, 2021) 🔥
45. [Evaluation of updating strategies for conformal predictive systems in the presence of extreme events](https://www.youtube.com/watch?v=Xgs0JqDw8lA) by Hugo Werner, Lars Carlsson, Ernst Ahlberg and and Henrik Boström (KTH Royal Institute of Technology, Sweden, 2021)
46. [Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data](https://www.youtube.com/watch?v=Wcm9Uw0YL8A) by Victor Chernozhukov (MIT, USA, 2019) 🔥
47. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (Jönköping University, Sweden, 2020) 🔥
48. [Class-wise confidence for debt prediction in real estate management](https://www.youtube.com/watch?v=ZVhA8LGXWpc) by Soundouss Messoudi (2021)
49. [How Nonconformity Functions and Difficulty of Datasets Impact the Efficiency of Conformal Classifiers](https://www.youtube.com/watch?v=lLtZkVwxMNw) by Marharyta Aleksandrova (2021)
50. [Nested conformal prediction and quantile out-of-bag ensemble methods](https://www.youtube.com/watch?v=NlUlelNWVMQ) by Chirag Gupta (Carnegie Mellon, 2020) 🔥
51. [Panel with Michael I. Jordan, Vladimir Vovk, and Larry Wasserman, moderated by Aaditya Ramdas](https://slideslive.com/38964850/panel-with-michael-i-jordan-vladimir-vovk-and-larry-wasserman-moderated-by-aaditya-ramdas?ref=account-folder-87373-folders) by Vladimir Vovk, Larry Wasserman, Michael I. Jordan, Aaditya Ramdas, ICML 2021 🔥
52. [Black-box uncertainty - Anastasios Angelopoulos](https://www.youtube.com/watch?v=jW-mbsVgcIc) by Anastasios Angelopoulos (Berkeley, USA, 2021) 🔥
53. [P.C. Mahalanobis Memorial Lectures 2020-21](https://www.isibang.ac.in/~statmath/pcm2020/) by Vladimir Vovk (Royal Holloway, UK, 2021)
54. [Rahul Vishwakarma: New Perspective on Machine Learning Predictions Under Uncertainty | SNIA Storage Developer Conference, Santa Clara 2019](https://www.youtube.com/watch?v=T-hG1JyAk4E) by Rahul Vishwakarma (2019)
55. [Fast conformal classification using influence functions](https://www.youtube.com/watch?v=LRwm976poDE) by Umang Bhatt, Adrian Weller and Giovanni Cherubin (Cambridge / Alan Turinig Institute, 2021).
56. [Adaptive Conformal Predictions for Time Series | ISDFS](https://www.youtube.com/watch?v=Yuxu9aUpVi0) by Margaux Zaffran (2022) TIME SERIES 🚀🔥[Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
57. [Recent progress in predictive inference](https://www.youtube.com/watch?v=FQxpdMME0qU) by Emmanuel Candes, Stanford University (2022)
58. [Conformalized Survival Analysis with Adaptive Cutoffs](https://youtu.be/bvZ0SZlnkv8) by Rina Foygel Barber, Zhimei Ren, Yu Gui and Rohan Hore, University of Chicago (2022)
59. [Calibrating probabilistic hierarchical forecasts with conformal predictions](https://www.youtube.com/watch?v=VNhgk8Q1VFg) by Daan Ferdinandusse (University of Amsterdam, 2022)
60. [Michael I. Jordan on Conformal Prediction](https://www.youtube.com/watch?v=kSGP4F_ZcBY) by Michael I. Jordan (Berkeley, 2022)
61. [Distribution-free Prediction: Exchangeability and Beyond](https://www.youtube.com/watch?v=HVZDLhW8Kxg) by Rina Foygel Barber (University of Chicago, 2022)
62. [Purdue Statistics Theme Seminar, Conformal Prediction in 2022](https://www.youtube.com/watch?v=pjhp1moPvO4) by Emmanuel Candes (Stanford, 2022)
63. [WILL MY ROBOT ACHIEVE MY GOALS? PREDICTING THE PROBABILITY THAT AN MDP POLICY REACHES A USER-SPECIFIED BEHAVIOR TARGET](https://arxiv.org/pdf/2211.16462.pdf) by Alexander Guyer and Thomas G. Dietterich (University of Oregon, 2022)
64. [Robust and Equitable Uncertainty Estimation](https://www.youtube.com/watch?v=Iznj13jP9ag) by Aaron Roth(2022)
65. [Conformal prediction under feedback covariate shift for biomolecular design](https://www.youtube.com/watch?v=AOyDjBSQjhk) by Clara Wong-Fannjiang (Berkeley, 2022)
66. [Conformal prediction in 2022](https://slideslive.com/38996063/conformal-prediction-in-2022?ref=speaker-43789) invited talk by Emmanuel Candes at NeurIPS2022 🔥
67. [Broadening the Scope of Conformal Inference](https://slideslive.com/38988210/broadening-the-scope-of-conformal-inference?ref=search-presentations-Conformal+prediction) by Michael I. Jordan (University of Berkeley, 2022) 🔥
68. [Paper Reading Group - Fortuna, a Library for Uncertainty Quantification](https://www.youtube.com/watch?app=desktop&v=S_QgP3jBCqw)
69. [CLIMB Evergreen talk with Emmanuel Candès: Conformal Inference when Data is not Exchangeable](https://www.youtube.com/watch?v=D60vtpnkhTU) TIME SERIES 🚀🔥
70. [Algo Hour – Conformal Inference of Counterfactuals and Individual Treatment Effects | Lihua Lei](https://www.youtube.com/watch?v=COW2QNBmEMw&t=2s)
71. ['MoroccoAI webinar - Dr. Soundouss Messoudi - ‘Confidence learning using conformal prediction’](https://www.youtube.com/watch?v=SypbjiBrt3Q)
72. [Emmanuel Candes - A Taste of Conformal Prediction](https://www.carmin.tv/en/video/a-taste-of-conformal-prediction) by Emmanuel Candes (2023)
73. [Foundations of Conformal Prediction - Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) 🔥
74. [Max Mergenthaler and Fede Garza - Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023) TIME SERIES 🚀🔥
75. [Uncertainty Quantification with the Fortuna library by Gianluca Detommaso (AWS)](https://www.youtube.com/watch?v=FEtD6lnsI58) by Gianluca Detommaso (Amazon, 2023)
76. [Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023)
77. [NISS/Merck Meetup on Conformal Inference: Advancing the Boundaries of Machine Learning 4.19.2023](https://www.youtube.com/watch?v=SRbqUae9-_o&t=5631s) (2023) 🔥
78. [Max Kuhn - The Post-Modeling Model to Fix the Model](https://www.youtube.com/watch?v=3omi4lm1da0) by Max Kuhn (2023)🔥
79. [ISDFS Talk: Robots that ask for help: Conformal Prediction for LLM Planners](https://www.youtube.com/watch?v=WnQopZKJsTw) by Anirudha Majumdar (Princeton/DeepMind) 🔥(2023)
80. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://www.youtube.com/watch?v=xvXrPdPb3Ko) by Allen Z Ren (2023) 🔥
81. [Conformal Prediction & Complex Data Analytics] ([https://www.youtube.com/watch?v=PbHsmPupFak](https://www.youtube.com/watch?v=PbHsmPupFak)) by Matteo Fontana (2023) 🔥
82. [Three Easy Steps to Understand Conformal Prediction (CP), Conformity Score, Python Implementation](https://www.youtube.com/watch?v=oqK6rM8fbkk) by (Dr. Data Science 2023) 🔥
83. [Rising Stars #8: Clara Wong-Fannjiang (Genentech) - Prediction-Powered Inference](https://www.youtube.com/watch?v=TlFpVpFx7JY)
84. [Rising Stars #10 - Special Series on Conformal Prediction: Isaac Gibbs (Stanford University) Conformal Inference with Conditional Guarantees](https://www.youtube.com/watch?v=rvYnR0FGxM4)
85. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://www.youtube.com/watch?v=QhvLaZ1aS6A) by Andreas Auer TIME SERIES 🚀🔥
86. [Introducing Sequential Predictive Conformal Inference (SPCI)](https://www.youtube.com/watch?v=NQHrTMZ2-XA&t=346) by Chen Xu (2023) TIME SERIES 🚀🔥
87. [Conformal Prediction Intervals: Empowering Executives for Informed Decision](https://www.youtube.com/watch?v=3StianQJZTk&t=26s) by Matthew Kolakowski (2023) 🔥
88. [Conformal Inference with Tidymodels - posit::conf(2023)](https://www.youtube.com/watch?v=vJ4BYJSg734) by Max Kuhn (2023)
89. [ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles](https://www.youtube.com/watch?v=8PwDHAITuwU) by Sangdon Park (2023)
90. [Leveraging conformal prediction for calibrated probabilistic time series forecast](https://www.youtube.com/watch?v=--WcrDRtrYk) by Inge van den Ende (Dexter Energy, 2023) TIME SERIES 🚀🔥
91. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://www.youtube.com/watch?v=Yq9cvqEk2K8&t=8s) by Kexin Huang (Stanford, 2023) 🔥🔥
92. [Selection by Prediction with Conformal p-values](https://www.youtube.com/watch?v=VHcCmkChwTk&t=5s) by Ying Jin (Stanford, 2023)
93. [Trustworthy Retrieval Augmented Chatbots Utilizing Conformal Predictors](https://www.youtube.com/watch?v=JnWXebWUEg4) by Shuo Li (UPenn, 2023).

## Papers

1. [Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination](https://pubs.acs.org/doi/10.1021/ci5001168) by Ulf Norinder, Lars Carlsson, Scott Boyer, and Martin Eklund (2014)
2. [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://arxiv.org/pdf/2009.14193.pdf) by Anastasios N. Angelopoulos, Stephen Bates, Jitendra Malik, & Michael I. Jordan (Berkeley, 2021) 🔥
3. [Conformal Prediction Under Covariate Shift](https://arxiv.org/abs/1904.06019) by Ryan Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas (Carnegie Mellon, Stanford, Chicago, 2019) 🔥
4. [Regression Conformal Prediction with Nearest Neighbours](https://arxiv.org/pdf/1401.3880.pdf) by Harris Papadopoulos, Vladimir Vovk and Alex Gammerman (Royal Holloway, UK, 2014) 🔥
5. [Nested conformal prediction and quantile out-of-bag ensemble methods](https://arxiv.org/pdf/1910.10562.pdf) by Chirag Gupta, Arun Kuchibhotla and Aaditya Ramdas (Carnegie Mellon, 2021) 🔥
6. [Cross-conformal predictive distributions](http://proceedings.mlr.press/v91/vovk18a.html) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin and Alexander Gammerman (Royal Holloway, UK, 2018) 🔥
7. [Criteria of Efficiency for Conformal Prediction](https://arxiv.org/pdf/1603.04416.pdf) by Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, and Alex Gammerman ((Royal Holloway, UK, 2016)
8. [Conformal Prediction for Simulation Models](https://benjaminleroy.github.io/documents/icml2021/conformal_prediction_for_simulation_models.pdf) by Benjamin LeRoy and Chad Schafer (Carnegie Mellon, 2021)
9. [Distribution-free, risk-controlling prediction sets](https://arxiv.org/pdf/2101.02703) Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik and Michael I Jordan (Berkeley, 2021) 🔥
10. [Conditional calibration for false discovery rate control under dependence](https://arxiv.org/abs/2007.10438) by William Fithian and Lihua Lei (Stanford, 2021)
11. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana and S. Vantini (2021) 🔥
12. [Regression conformal prediction with random forests](https://link.springer.com/content/pdf/10.1007%2Fs10994-014-5453-0.pdf) by Ulf Johansson, Henrik Boström, Tuve Löfström and Henrik Linusson (2014)
13. [A conformal prediction approach to explore functional data](https://arxiv.org/pdf/1302.6452.pdf) by Jing Lei, Alessandro Rinaldo, Larry Wasserman (Carnegie Mellon, 2013)
14. [An electronic nose-based assistive diagnostic prototype for lung cancer detection with conformal prediction](https://pure.royalholloway.ac.uk/portal/files/37171406/Measurement_Manuscript_unmarked_R4.pdf) by Xianghao Zhana,c, Zhan Wanga, Meng Yangb, Zhiyuan Luod, You Wanga, Guang Li (2020)
15. [Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework](https://pubmed.ncbi.nlm.nih.gov/28335598/) by Martin Lindh, A. Karlén, Ulf Norinder (2017)
16. [The application of conformal prediction to the drug discovery process](https://link.springer.com/article/10.1007%2Fs10472-013-9378-2) by Martin Eklund, Ulf Norinder, Scott Boyer & Lars Carlsson (2014) 🔥
17. [Distributional conformal prediction](https://arxiv.org/pdf/1909.07889.pdf) by Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu (2021) 🔥
18. [Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction](https://link.springer.com/content/pdf/10.1007%2F978-3-662-44722-2_29.pdf) by James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, and Alexander Gammerman (2009)
19. [Conformal prediction interval estimation and applications to day-ahead and intraday power markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath, Florian Ziel (2019) 🔥
20. [The application of conformal prediction to the drug discovery process](https://link.springer.com/article/10.1007%2Fs10472-013-9378-2) by Martin Eklund, Ulf Norinder, Scott Boyer & Lars Carlsson (2013)
21. [Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction](https://link.springer.com/chapter/10.1007%2F978-3-662-44722-2_29) by James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, Alexander Gammerman (Royal Holloway, UK, 2014)
22. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana1 and Simone Vantini (Politecnico di Milano, Italy, 2021)
23. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
24. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (Yale, 2020)
25. [Predicting skin sensitizers with confidence - Using conformal prediction to determine applicability domain of GARD](https://pubmed.ncbi.nlm.nih.gov/29374571/) by Andy Forreryd, Ulf Norinder, Tim Lindberg, Malin Lindstedt (2018)
26. [Binary classification of imbalanced datasets using conformal prediction](https://pubmed.ncbi.nlm.nih.gov/28135672/) by Ulf Norinder, Scott Boyer (2017)
27. [Discretized conformal prediction for efficient distribution-free inference](https://arxiv.org/pdf/1709.06233.pdf) by Wenyu Chen, Kelli-Jean Chun, and Rina Foygel Barber (2017)
28. [Validity, consonant plausibility measures, and conformal prediction](https://www.sciencedirect.com/science/article/abs/pii/S0888613X21001195?via%3Dihub) by Leonardo Cella. and Ryan Martin (2021)
29. [Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays](https://pubmed.ncbi.nlm.nih.gov/27152554/) by Ulf Norinder, Scott Boyer (2016)
30. [Conformal prediction to define applicability domain – A case study on predicting ER and AR binding](https://www.tandfonline.com/doi/full/10.1080/1062936X.2016.1172665) by U. Norinder, A. Rybacka, P.Andersson (2016)
31. [Conformal prediction of biological activity of chemical compounds](https://link.springer.com/article/10.1007%2Fs10472-017-9556-8) by Paolo Toccaceli, Ilia Nouretdinov, Alex Gammerman (Royal Holloway, UK, 2017) 🔥
32. [Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination](https://pubmed.ncbi.nlm.nih.gov/25559551/) by Ulf Norinder, Lars Carlsson, Scott Boyer, Martin Eklund (2015)
33. [Aggregated Conformal Prediction](https://link.springer.com/chapter/10.1007%2F978-3-662-44722-2_25) by Lars CarlssonMartin EklundUlf Norinder (2014)
34. [Interpretation of Conformal Prediction Classification Models](https://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_27) by Ernst Ahlberg, Ola Spjuth, Catrin Hasselgren, Lars Carlsson (2015)
35. [Cross-Conformal Prediction with Ridge Regression](https://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_21) by Harris Papadopoulos (2015)
36. [Sparse conformal prediction for dissimilarity data](https://link.springer.com/article/10.1007%2Fs10472-014-9402-1) by Frank-Michael Schleif, Xibin Zhu and Barbara Hammer (2015)
37. [Effective utilization of data in inductive conformal prediction using ensembles of neural networks](https://ieeexplore.ieee.org/document/6706817) by Tuve Löfström, Ulf Johansson and Henrik Boström (2013)
38. [Beyond the Basic Conformal Prediction Framework](https://www.sciencedirect.com/science/article/pii/B978012398537800002X?via%3Dihub) by Vladimir Vovk (2014)
39. [An electronic nose-based assistive diagnostic prototype for lung cancer detection with conformal prediction](https://pure.royalholloway.ac.uk/portal/files/37171406/Measurement_Manuscript_unmarked_R4.pdf) by Xianghao Zhan, Zhan Wang, Meng Yang, Zhiyuan Luo, You Wang, Guang Li (Stanford, Royal Holloway, China University of Mining and Technology, 2020)
40. [Predicting with confidence: Using conformal prediction in drug discovery](https://jpharmsci.org/article/S0022-3549(20)30589-X/fulltext) by Jonathan Alvarsson, Staffan Arvidsson McShane, Ulf Norinder, Ola Spjuth (2021) 🔥
41. [Inductive conformal prediction for silent speech recognition](https://pubmed.ncbi.nlm.nih.gov/32120355/) by Ming Zhang, You Wang, Zhang Wei, Meng Yang, Zhiyuan Luo, Guang Li (2020)
42. [Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0325-4) by Nicolas Bosc, Francis Atkinson, Eloy Felix, Anna Gaulton, Anne Hersey and Andrew R. Leach (2019)
43. [Deep Conformal Prediction for Robust Models](https://link.springer.com/chapter/10.1007%2F978-3-030-50146-4_39) by Soundouss Messoudi, Sylvain Rousseau and Sébastien Destercke (2020)
44. [Strong validity, consonance, and conformal prediction](https://arxiv.org/abs/2001.09225) by Leonardo Cella and Ryan Martin (2020)
45. [Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules](https://pubs.acs.org/doi/10.1021/acs.chemrestox.0c00253) by Anke Wilm, U. Norinder, M. Agea, Christina de Bruyn Kops, Conrad Stork, J. Kühnl, J. Kirchmair (2020)
46. [Conformal prediction based active learning by linear regression optimization](https://www.sciencedirect.com/science/article/abs/pii/S0925231220300461?via%3Dihub) by Sergio Matiz, Kenneth E.Barner (2020)
47. [Conformal prediction intervals for the individual treatment effect](https://arxiv.org/pdf/2006.01474.pdf) by Danijel Kivaranovic, Robin Ristl, Martin Poschb, Hannes Leeb (2020)
48. [Nearest neighbor based conformal prediction](https://www.f08.uni-stuttgart.de/.content/media/downloads/Mathematik/mathematische_berichte/2020/2020-002.pdf) by László Györfi and Harro Walk (2020)
49. [Concepts and Applications of Conformal Prediction in Computational Drug Discovery](https://arxiv.org/pdf/1908.03569.pdf) by Isidro Cortés-Ciriano and Andreas Bender (2019) 🔥
50. [Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project](https://academic.oup.com/mutage/article/34/1/33/5239859) by Ulf Norinder, Ernst Ahlberg, Lars Carlsson (2018)
51. [Nested Conformal Prediction and the Generalized Jackknife](https://arxiv.org/pdf/1910.10562v1.pdf) by Arun Kuchibhotla and Aaditya Ramdas (Carnegie Mellon, 2019)
52. [Predictive inference with the jackknife+](https://arxiv.org/abs/1905.02928) by Rina Foygel Barber, Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani (2020) 🔥
53. [Nonparametric predictive distributions based on conformal prediction](https://link.springer.com/article/10.1007%2Fs10994-018-5755-8) by Vladimir Vovk, Jieli Shen, Valery Manokhin and Min-ge Xie (Royal Holloway, UK, Rutgers, USA, 2018) 🔥
54. [A Distribution-Free Test of Covariate Shift Using Conformal Prediction](https://arxiv.org/pdf/2010.07147.pdf) by Xiaoyu Hu and Jing Lei (Peking Univerity, China and Carnegie Mellon, USA, 2020) 🔥
55. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
56. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (2020)
57. [Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction](https://pubmed.ncbi.nlm.nih.gov/30908915/) by Ulf Norinder, Fredrik Svensson
58. [Conformal prediction of HDAC inhibitors](https://www.tandfonline.com/doi/abs/10.1080/1062936X.2019.1591503?journalCode=gsar20) by U. Norinder, J.J.Navaka, E. Lopez-Lopez, D. Mucs & J.L. Medina-Franco (2019)
59. [Computing Full Conformal Prediction Set with Approximate Homotopy](https://arxiv.org/pdf/1909.09365.pdf) by Eugene Ndiaye, Ichiro Takeuchi (2019)
60. [Conformal Prediction Based on Raman Spectra for the Classification of Chinese Liquors](https://journals.sagepub.com/doi/10.1177/0003702819831017) by Jiao Gu, Huaibo Liu, Chaoqun Ma, Lei Li, Chun Zhu, Christ Glorieux, Guoqing Chen (2019)
61. [Efficient and minimal length parametric conformal prediction regions](https://export.arxiv.org/pdf/1905.03657) by Daniel Eck and Forrest Crawford (2019)
62. [Conformal Prediction for Students’ Grades in a Course Recommender System](http://proceedings.mlr.press/v105/morsomme19a/morsomme19a.pdf) by Raphael Morsomme and Evgueni Smirnov (2019)
63. [Efficient iterative virtual screening with Apache Spark and conformal prediction](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0265-z) by Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure and Ola Spjuth (2018)
64. [Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction](https://pubmed.ncbi.nlm.nih.gov/30459617/) by Samuel Lampa, Jonathan Alvarsson, Staffan Arvidsson Mc Shane, Arvid Berg, Ernst Ahlberg, Ola Spjuth (2018)
65. [Maximizing gain in high-throughput screening using conformal prediction](https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-018-0260-4.pdf) by Fredrik Svensson, Avid M. Afzal1, Ulf Norinder and Andreas Bender (2018)
66. [Conformalized Survival Analysis](https://arxiv.org/pdf/2103.09763.pdf) by Emmanuel Candès, Lihua Lei and Zhimei Ren (2021) [R-Code](https://github.com/valeman/cfsurv_paper) 🔥
67. [Random Forest Prediction Intervals](https://haozhestat.github.io/files/manuscript_RFIntervals_FinalVersion.pdf) by Haozhe Zhang†, Joshua Zimmerman†, Dan Nettleton† and Daniel J. Nordman† (Iowa State University, USA, 2019)
68. [Conformal Training: Learning Optimal Conformal Classifiers | DeepMind](https://arxiv.org/pdf/2110.09192.pdf) by David Stutz (DeepMind), Krishnamurthy Dvijotham, Ali Taylan Cemgil and Arnaud Doucet (2021)
69. [Comparing the Bayes and typicalness frameworks](https://link.springer.com/content/pdf/10.1007%2F3-540-44795-4_31.pdf) by Thomas Melluish, Craig Saunders, Ilia Nouretdinov, and Volodya Vovk (Royal Holloway, UK, 2001). 🔥
70. [Large-scale probabilistic predictors with and without guarantees of validity](https://papers.nips.cc/paper/2015/file/a9a1d5317a33ae8cef33961c34144f84-Paper.pdf) by Vladimir Vovk, Ivan Petej, and Valentina Fedorova (Royal Holloway, Yandex, NeurIPS) 🔥
71. [Inductive conformal prediction for silent speech recognition](https://iopscience.iop.org/article/10.1088/1741-2552/ab7ba0) by Ming Zhang, You Wang, Wei Zhang, Meng Yang, Zhiyuan Luo and Guang Li (2020)
72. [Conformal Prediction using Conditional Histograms](https://papers.nips.cc/paper/2021/file/31b3b31a1c2f8a370206f111127c0dbd-Paper.pdf) by Matteo Sesia and Yaniv Romano (NeurIPS 2021 paper). [code](https://github.com/msesia/chr) 🔥
73. [Valid prediction intervals for regression problems](https://arxiv.org/abs/2107.00363) by Nicolas Dewolf, Bernard De Baets, Willem Waegeman (2021) 🔥
74. [Application of conformal prediction interval estimations to market makers’ net positions](http://proceedings.mlr.press/v128/wisniewski20a.html) by Wojciech Wisniewski, David Lindsay, Sian Lindsay (Royal Holloway, UK, 2020)
75. [Locally Valid and Discriminative Prediction Intervals for Deep Learning Models](https://papers.nips.cc/paper/2021/hash/46c7cb50b373877fb2f8d5c4517bb969-Abstract.html) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (NeurIPS, 2021) 🔥
76. [Distribution-Free Federated Learning with Conformal Predictions](https://arxiv.org/pdf/2110.07661.pdf) by Charles Lu and Jayashree Kalpathy-Cramer (2022)
77. [Coreset-based Conformal Prediction for Large-scale Learning](https://proceedings.mlr.press/v105/riquelme-granada19a.html) by Nery Riquelme-Granada, Khuong Nguyen, Zhiyuan Luo (Royal Holloway, UK, 2019)
78. [Fast probabilistic prediction for kernel SVM via enclosing balls](https://proceedings.mlr.press/v128/riquelme-granada20a.html) by Nery Riquelme-Granada, Khuong Nguyen, Zhiyuan Luo (Royal Holloway, UK, 2020)
79. [Conformalized density- and distance-based anomaly detection in time-series data](https://arxiv.org/abs/1608.04585) by Evgeny Burnaev, Vladislav Ishimtsev (2016)
80. [Predictive Inference with Weak Supervision](https://arxiv.org/pdf/2201.08315.pdf) by Maxime Cauchois, Suyash Gupta, Alnur Ali and John Duchi (Stanford, 2022)
81. [Conformal Prediction in Clinical Medical Sciences](https://link.springer.com/10.1007/s41666-021-00113-8) by Janette Vazquez and Julio C. Facelli University of Utah, 2022)
82. [Provably Improving Expert Predictions with Conformal Prediction](https://arxiv.org/pdf/2201.12006.pdf) by Eleni Straitouri, Lequng Wang, Nastaran Okati and Manuel Gomez Rodriguez (Max Planck Institute for Software Systems / Cornell University, 2021).
83. [Conformal predictive distributions with kernels](https://arxiv.org/abs/1710.08894) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Royal Holloway, UK, 2017)
84. [Multi-class probabilistic classification using inductive and cross Venn–Abers predictors](https://proceedings.mlr.press/v60/manokhin17a.html) by Valery Manokhin (Royal Holloway, UK, 2017). 🔥
85. [Computationally efficient versions of conformal predictive distributions](https://arxiv.org/abs/1911.00941) by Vladimir Vovk, Ivan Petej, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Royal Holloway, UK, 2019).
86. [Cover your cough: detection of respiratory events with confidence using a smartwatch](https://proceedings.mlr.press/v91/nguyen18a.html) by Khuong An Nguyen, Zhiyuan Luo (Royal Holloway, 2019).
87. [Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction](https://www.tandfonline.com/doi/full/10.1080/23270012.2022.2031324) by Ulf Norinder and Petra Norinder (2022)
88. [Conformal Prediction for the Design Problem](https://arxiv.org/pdf/2202.03613.pdf) by Clara Fannjianga, Stephen Batesa, Anastasios Angelopoulosa, Jennifer Listgartena and Michael I. Jordan (Berkeley, 2022) 🔥
89. [Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging](https://arxiv.org/pdf/2202.05265.pdf) by Anastasios N. Angelopoulos, Amit Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano (Berkeley and Technion, 2022) 🔥
90. [Conformal predictive decision making](http://proceedings.mlr.press/v91/vovk18b/vovk18b.pdf) by Vladimir Vovk and Claus Bendtsen (2018).
91. [The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting](https://arxiv.org/pdf/2202.04166.pdf) by Alnur Ali1, Maxime Cauchois and John C. Duchi (Stanford, 2022)
92. [E-values: Calibration, combination, and applications](https://arxiv.org/pdf/1912.06116.pdf) by Vladimir Vovk (Royal Holloway) and Ruodu Wang (University of Waterloo) (2019)
93. [Conformal Prediction Sets with Limited False Positives](https://arxiv.org/pdf/2202.07650.pdf) by Adam Fisch, Tal Schuster, Tommi Jaakkola and Regina Barzilay [code](https://github.com/ajfisch/conformal-fp) (MIT / Google Research, 2022) 🔥
94. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/pdf/2202.07282.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (EDF / INRIA / CMAP, France, 2022) 🔥 [Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
95. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2202.08756v1.pdf) by Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen (Arctic University of Norway, 2022) TIME SERIES 🚀🔥 [Python Code](https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression)
96. [Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors](http://proceedings.mlr.press/v60/arvidsson17a/arvidsson17a.pdf) by Staffan Arvidsson, Ola Spjuth, Lars Carlsson and Paolo Toccaceli (University of Uppsala, Astra Zeneca, Royal Holloway, 2017)
97. [Probabilistic Prediction in scikit-learn](https://www.diva-portal.org/smash/get/diva2:1603345/FULLTEXT01.pdf) by Sweidan, Dirar and Ulf Johansson. 🔥
98. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://arxiv.org/pdf/2205.09095.pdf) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🚀🔥
99. [Valid model-free spatial prediction](https://arxiv.org/pdf/2006.15640.pdf) by Huiying Mao, Ryan Martin and Brian J Reich (2020)
100. [Conformal Prediction with Temporal Quantile Adjustments](https://arxiv.org/pdf/2205.09940.pdf) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (2022) TIME SERIES 🚀🔥
101. [Calibration of Natural Language Understanding Models with Venn–ABERS Predictors](https://arxiv.org/pdf/2205.10586.pdf) by Patrizio Giovannotti (Royal Holloway, UK, 2022) NLP
102. [Conformal prediction interval for dynamic time-series](https://proceedings.mlr.press/v139/xu21h.html) by Chen Xu, Yao Xie (Georgia Tech, 2021) TIME SERIES 🚀🔥
103. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES 🚀🔥
104. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) TIME SERIES 🚀🔥[Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
105. [Conformal Time-Series Forecasting](https://proceedings.neurips.cc/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf) by Kamile Stankeviciu te and Ahmed M. Alaa (2021) TIME SERIES 🚀🔥
106. [Efficient Conformal Prediction via cascaded inference with expanded admission](https://arxiv.org/pdf/2007.03114.pdf) by Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay (MIT 2021]. [Python Code](https://github.com/ajfisch/conformal-cascades)
107. [Split Localized Conformal Prediction](https://arxiv.org/abs/2206.13092) by Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu (University of Texas, 2022). [Python Code](https://github.com/valeman/SLCP)
108. [Three Applications of Conformal Prediction for Rating Breast Density in Mammography](https://arxiv.org/pdf/2206.12008.pdf) by Charles Lu, Ken Chang, Praveer Singh, Jayashree Kalpathy-Crame (2022)
109. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) Python Code TIME SERIES 🚀🔥
110. [Recommendation systems with distribution-free reliability guarantees](https://arxiv.org/pdf/2207.01609.pdf)) by Anastasious Angelopolous, Karl Krauth, Stephen Bates, Yixin Wang and Michael I. Jordan (Berkeley 2022)
111. [Model Agnostic Conformal Hyperparameter Optimization](https://arxiv.org/pdf/2207.03017.pdf) by Riccardo Doyle (Spotify, 2022)
112. [Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets](https://arxiv.org/pdf/2207.02238.pdf) by Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz (2022) [code](https://github.com/clu5/lumbar-conformal) 🔥
113. [Conformal Off-Policy Prediction in Contextual Bandits](https://arxiv.org/pdf/2206.04405.pdf) by Muhammad Faaiz Taufiq, Jean-François Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet (Oxford, 2022) [Video presentation](https://youtu.be/K5RAjP1Ze30)
114. [Training Uncertainty-Aware Classifiers with Conformalized Deep Learning](https://arxiv.org/abs/2205.05878) by Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou (Technion, USCLA 2022) [Video presentation](https://www.youtube.com/watch?v=RJ7ShciZq2s); [Code](https://github.com/bat-sheva/conformal-learning)
115. [Semantic uncertainty intervals for disentangled latent space](https://arxiv.org/pdf/2207.10074.pdf) by Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, and Phillip Isola (Unversity of Berkeley, Technion, 2022) 🔥
116. [MAPIE: an open-source library for distribution-free uncertainty quantification](https://arxiv.org/pdf/2207.12274.pdf) by Vianney Taquet, Vincent Blot, Thomas Morzadec, Louis Lacombe, Nicolas Brunel (Quantmetry, France, 2022)
117. [CODiT: Conformal Out-of-Distribution Detection in Time- Series Data](https://arxiv.org/pdf/2207.11769.pdf) by Ramneet Kaur et.al., Unibersity of Pensylvania (2022). [Code](https://github.com/kaustubhsridhar/time-series-OOD) TIME SERIES 🚀🔥🔥
118. [Confident Adaptive Language Modeling](https://arxiv.org/pdf/2207.07061.pdf) by Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler (Google, MIT, 2022j
119. [Probabilistic Conformal Prediction Using Conditional Random Samples](https://arxiv.org/pdf/2206.06584.pdf) by Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei (Columbia University, 2020) [Code](https://github.com/Zhendong-Wang/Probabilistic-Conformal-Prediction)
120. [A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tome and Jose Moreira (University of Aveiro, 2022) TIME SERIES 🚀🔥
121. [A novel Deep Learning approach for one-step Conformal Prediction approximation](https://arxiv.org/pdf/2207.12377.pdf) by Julia A. Meister, Khuong An Nguyen, Stelios Kapetanakis and Zhiyuan Luo (University of Brighton, UK, 2022) 🔥
122. [Conformal Risk Control](https://arxiv.org/pdf/2208.02814.pdf) by Anastasious Angelopolous, Stephen Bates, Adam Fisch, Lihua Lei and Tal Schuster (Berkeley, Stanford, MIT and Google Research, 2022) 🔥
123. [CD-split and HPD-split: Efficient Conformal Regions in High Dimensions](https://www.jmlr.org/papers/volume23/20-797/20-797.pdf) by Rafael Izbicki, Gilson Shimizu, Rafael B. Stern (San Carlos University Brazil, 2022)
124. [Flexible distribution-free conditional predictive bands using density estimators](https://proceedings.mlr.press/v108/izbicki20a.html) by Rafael Izbicki, Gilson Shimizu, and Rafael B. Stern (San Carlos University Brazil, 2020)
125. [Split Conformal Prediction for Dependent Data](https://arxiv.org/abs/2203.15885) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano (IMPA, Rio de Janeiro, Brazil, 2022)
126. [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://arxiv.org/pdf/2208.08401.pdf) by Isaac Gibbs and Emmanual Candes (Stanford, 2022) 🔥
127. [A General Framework For Multi-step Ahead Adaptive Conformal Heteroscedastic Time Series Forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tomé, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES 🚀🔥
128. [Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning](https://copa-conference.com/papers/COPA2022_paper_13.pdf) by Alice E. Ashby, Julia A. Meister, Khuong An Nguyen, Zhiyuan Luo, Werner Gentzk (University of Brighton, 2022)
129. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/papers/COPA2022_paper_10.pdf) by Amr Alkhatib, Henrik Bostroem and Ulf Johansson (2022)
130. [Conformal prediction for hypersonic flight vehicle classification](https://www.copa-conference.com/papers/COPA2022_paper_1.pdf) by Zepu Xi, Xuebin Zhuang, Hongbo Chen (Yat-sen University, Guangzhou, China, 2022) [Slides](https://copa-conference.com/presentations/Zepu.pdf)
131. [Robust Gas Demand Forecasting With Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
132. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath and Florian Ziel (2020)
133. [Conformal Prediciton beyond exchangeability](https://arxiv.org/abs/2202.13415) by Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani (2022)
134. [Robust Gas Demand Forecasting with Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
135. [Split conformal prediction for dependant data](https://arxiv.org/pdf/2203.15885.pdf) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos and João Vitor Romano (2022)
136. [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://arxiv.org/pdf/2208.11111.pdf) by Ziyi Liang, Matteo Sesia, Wenguang Sun (UCLA, 2022)
137. [Conformal Prediction Bands for Two-Dimensional Functional Time Series](https://arxiv.org/pdf/2207.13656.pdf) by Niccolo` Ajroldia, Jacopo Diquigiovannib, Matteo Fontanac, Simone Vantinia (2022)
138. [Conformal prediction of small-molecule drug resistance in cancer cell lines](https://copa-conference.com/papers/COPA2022_paper_22.pdf) by Saiveth Hernandez-Hernandez, Sachin Vishwakarma and Pedro Ballester (2022)
139. [Ellipsoidal conformal inference for Multi-Target Regression](https://copa-conference.com/papers/COPA2022_paper_7.pdf) by Soundouss Messoudi, Sebastien Destercke, Sylvain Rousseau (2022) [Slides](https://copa-conference.com/presentations/soundouss.pdf)
140. [Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey](https://arxiv.org/abs/2209.03580) by Sophia Sun (UCLA, 2022)
141. [Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9103229) by Håkan Wieslander , Philip J. Harrison, Gabriel Skogberg, Sonya Jackson, Markus Fridén, Johan Karlsson, Ola Spjuth, and Carolina Wählby (2021)
142. [Audio–visual domain adaptation using conditional semi-supervised Generative Adversarial Networks](https://www.sciencedirect.com/science/article/pii/S0925231219316170) by Christos Athanasiadis, Enrique Hortal, Stylianos Asteriadis (2022)
143. [Conformal Prediction is Robust to Label Noise](https://arxiv.org/pdf/2209.14295.pdf) by Bat-Sheva Einbinder, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano (2022)
144. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🔥
145. [Batch Multivalid Conformal Prediction](https://arxiv.org/abs/2209.15145) by Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth (Stanford, university of Pensylvania, 2022)
146. [Selection by Prediction with Conformal p-values](https://arxiv.org/pdf/2210.01408.pdf) by Ying Jin1 and Emmanuel J. Candes, (Stanford, 2022) [Video](https://www.youtube.com/watch?v=YEpvHsHCCvQ) [code](https://github.com/ying531/selcf_paper)
147. [Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction](https://arxiv.org/pdf/2210.03067v1.pdf) by Sangwoo Park, Kfir M. Cohen, Osvaldo Simeone (2022)
148. [Conformalized Fairness via Quantile Regression](https://web7.arxiv.org/pdf/2210.02015.pdf) by Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang (University of Alberta, Noah Arc Huawei, 2022)
149. [Test-time recalibration of conformal predictors under distribution shift based on unlabeled examples](https://arxiv.org/pdf/2210.04166.pdf) by Fatih Furkan Yilmaz, and Reinhard Heckel (Rice University / University of Munuch, 2022)
150. [Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains](https://arxiv.org/abs/2210.02271) by Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh Halappanavar (2022)
151. [Predictive inference with feature conformal prediction](https://arxiv.org/pdf/2210.00173.pdf) by Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan (Tsinghua University, Mila - Quebec AI Institute, Shanghai Artificial Intelligence Laboratory, Shanghai Qi Zhi Institute, 2022) [code](https://github.com/AlvinWen428/FeatureCP) 🔥
152. [Constructing Prediction Intervals with Neural Networks: An Empirical Evaluation of Bootstrapping and Conformal Inference Methods](https://arxiv.org/pdf/2210.05354.pdf) by Alex Contarino, Christine Schubert Kabban, Chancellor Johnstone and Fairul Mohd-Zaid (2022)
153. [Spatio-Temporal Wildfire Prediction using Multi-Modal Data](https://arxiv.org/pdf/2207.13250.pdf) by Chen Xu1, Yao Xie, Daniel A. Zuniga Vazquez, Rui Yao, and Feng Qiu (2022)
154. [Calibrating AI models for few-shot demodulation via conformal prediction](https://arxiv.org/pdf/2210.04621.pdf) Kfir M. Cohen1, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai (2022)
155. [Test-time recalibration of conformal predictors under distribution shift based on unlabeled examples](https://arxiv.org/pdf/2210.04166.pdf) [Code](https://github.com/MLI-lab/recalibrating_conformal_prediction)
156. [Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction](https://arxiv.org/pdf/2210.10161.pdf) by Wenlu Tang, Guohao Shen, Yuanyuan Lin and Jian Huang (The Hong Kong Polytechnic University, 2022)
157. [Safe Planning in Dynamic Environments using Conformal Prediction](https://arxiv.org/pdf/2210.10254.pdf) by Lars Lindemann, Matthew Cleaveland∗, Gihyun Shim, and George J. Pappas (University of Pensylvania, 2022)
158. [Conformal prediction under feedback covariate shift for biomolecular design](https://www.pnas.org/doi/10.1073/pnas.2204569119) by Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos,and Michael I. Jordan (2022) 🔥
159. [Conformal Predictor for Improving Zero-shot Text Classification Efficiency](https://arxiv.org/pdf/2210.12619.pdf) by Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani (Saleforce AI Research and Hugging Face, 2022)
160. [Bayesian Optimization with Conformal Coverage Guarantees](https://arxiv.org/pdf/2210.12496.pdf) by Samuel Stanton, Wesley Maddox and Andrew Gordon Wilson (Genentech, New York University, 2022) [Code](https://github.com/samuelstanton/conformal-bayesopt) 🔥
161. [Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability](https://arxiv.org/pdf/2210.16049.pdf) by Ibai Lanaa, Ignacio (In ̃aki) Olabarrietaa, Javier Del Sera (2022)
162. [Training Uncertainty-Aware Classifiers with Conformalized Deep Learning](https://openreview.net/pdf?id=NaZwgxp-mT_) by Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia and Yanfei Zhou (Technion/UCLA, NeurIPS 2022 paper) 🔥
163. [Conformalized Fairness via Quantile Regression](https://openreview.net/pdf?id=rwyISFoSmXd) by Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang (University of Alberta, Huawei Noah’s Ark Lab Canada, NeurIPS 2022 paper) [Code](https://github.com/Lei-Ding07/Conformal_Quantile_Fairness) 🔥
164. [Engineering Uncertainty Representations to Monitor Distribution Shifts](https://openreview.net/pdf?id=Koug1i2HpH) by Thomas Bonnier and Benjamin Bosch (Société Générale, 2022)
165. [CONffusion: CONFIDENCE INTERVALS FOR DIFFUSION MODELS](https://arxiv.org/pdf/2211.09795.pdf) [Project](https://www.vision.huji.ac.il/conffusion/)[Code](https://github.com/eliahuhorwitz/Conffusion) by Eliahu Horwitz, Yedid Hoshen (Hebrew University of Jerusalem, 2022) 🔥
166. [Semantic uncertainty intervals for disentangled latent spaces](https://swamiviv.github.io/semantic_uncertainty_intervals/) by Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola (MIT, Berkeley, Technion, 2022) 🔥
167. [But are you sure? An uncertainty-aware perspective on explainable AI](https://assets.amazon.science/bf/d0/14c3eb614699909dae8a092b9492/but-are-you-sure-an-uncertainty-aware-perspective-on-explainable-ai.pdf) by Charlie Marx, Youngsuk Park, Hilaf Hasson, Yuyang (Bernie) Wang, Stefano Ermon, Jun Huan (2022)
168. [Calibrating AI Models for Wireless Communications via Conformal Prediction](https://arxiv.org/pdf/2212.07775.pdf) by Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone and Shlomo Shamai (2022)
169. [Predicting Endocrine Disruption Using Conformal Prediction – A Prioritization Strategy to Identify Hazardous Chemicals with Confidence](https://pubs.acs.org/doi/10.1021/acs.chemrestox.2c00267) by Maria Sapounidou, Ulf Norinder and Patrick Andersson (2022)
170. [Conformal Loss-Controlling Prediction](https://arxiv.org/pdf/2301.02424.pdf) by Di Wang, Ping Wang, Zhong Ji, Xiaojun Yang, Hongyue Li (2023)
171. [ROBUST AND SCALABLE UNCERTAINTY ESTIMATION WITH CONFORMAL PREDICTION FOR MACHINE-LEARNED INTERATOMIC POTENTIALS](https://arxiv.org/pdf/2208.08337.pdf) [Code](https://github.com/valeman/conformal_prediction_in_latent_space) by Yuge Hu, Joseph Musielewicz, Zachary Ulissi, Andrew J. Medford (Georgia Institute of Technology/Carnegie Mellon University, 2022)
172. [Clustering of Trajectories using Non-Parametric Conformal DBSCAN Algorithm](https://sites.rutgers.edu/jie-gao/wp-content/uploads/sites/375/2022/03/conformal-DBSCAN-compressed.pdf) by Haotian Wang, Jie Gao, Min-ge Xie Rutgers University, 2022)
173. [But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI](https://assets.amazon.science/bf/d0/14c3eb614699909dae8a092b9492/but-are-you-sure-an-uncertainty-aware-perspective-on-explainable-ai.pdf) by Charlie Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Jun Huan (2022)
174. [Prediction-Powered Inference](https://arxiv.org/pdf/2301.09633.pdf) by Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic (Universify of Berkeley, 2022) [code](https://github.com/aangelopoulos/ppi_py) 🔥
175. [Conformal Prediction for Trustworthy Detection of Railway Signals](https://arxiv.org/pdf/2301.11136.pdf) by Leo Andeol, Thomas Fel, Florence de Grancey, Luca Mossina (Institute de Mathematiques de Toulouse, SCNF, 2022)
176. [Conformal inference is (almost) free for neural networks trained with early stopping](https://arxiv.org/pdf/2301.11556.pdf) by Ziyi Liang, Yanfei Zhou†, Matteo Sesia (University of Southern California, 2022)
177. [PAC Prediction Sets for Large Language Models of Code](https://www.seas.upenn.edu/~akhakhar/pacsetllm.pdf) by Adam Khakhar, Stephen Mell, Osbert Bastani (University of Pennsylvania, 2023)
178. [Physics Constrained Motion Prediction with Uncertainty Quantification](https://arxiv.org/pdf/2302.01060.pdf) by Renukanandan Tumu, Lars Lindemann†, Truong Nghiem, Rahul Mangharam, (2023)
179. [Accelerating difficulty estimation for conformal regression forests](https://link.springer.com/content/pdf/10.1007/s10472-017-9539-9.pdf) by Henrik Bostroem, Henrik Linusson, Tuve Loefstroem, Ulf Johansson (2017)
180. [Conformal prediction for exponential families and generalized linear models](https://arxiv.org/pdf/1905.03657v1.pdf)
181. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) by Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023) [Code](https://github.com/Sulam-Group/k-rcps)
182. [Localized Conformal Prediction: A Generalized Inference Framework to Conformal Prediction](https://arxiv.org/pdf/2106.08460.pdf) [Code](https://github.com/LeyingGuan/LCPexperiments) [R package](https://github.com/LeyingGuan/LCP)
183. [From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling](https://arxiv.org/pdf/2302.05304.pdf) by Ernsting et.al. (2023)
184. [Conformal prediction for STL runtime verification](https://arxiv.org/pdf/2211.01539.pdf) by Lars Lindemann, Xin Qin, Jyotirmoy V. Deshmukh, George J. Pappas (University of Pennsylvania/University of Southern California, 2022)
185. [Adaptive Conformal Prediction for Motion Planning among Dynamic Agents](https://arxiv.org/pdf/2212.00278.pdf) by Anushri Dixit, Lars Lindemann, Skylar Wei, Matthew Cleaveland, George J Pappas, Joel W Burdick (California Institute of Technology/University of Pennsylvania, 2022)
186. [Classification with Valid and Adaptive Coverage](https://proceedings.neurips.cc/paper/2020/file/244edd7e85dc81602b7615cd705545f5-Paper.pdf) [Code](https://github.com/msesia/arc) by Yaniv Romano, Matteo Sesia, Emmanuel Candes (Neurips, 2020) 🔥
187. [Risk Control for Online Learning Models](https://openreview.net/forum?id=uqLDy0HGPR7) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (2023)
188. [Derandomized novelty detection with FDR control via conformal e-values](https://arxiv.org/pdf/2302.07294v1.pdf) by Meshi Bashari, Amir Epstein, Yaniv Romano, and Matteo Sesia (2023) [code](https://github.com/Meshiba/derandomized-novelty-detection)
189. [Sensititivty analysis of individual treatment effects: A robust conformal inference approach](https://www.pnas.org/doi/epdf/10.1073/pnas.2214889120) [Code](https://github.com/ying531/cfsensitivity_paper) by Ying Jin, Zhimei Ren and Emmanual Candes (2023)
190. [Improving Adaptive Conformal Prediction Using Self-Supervised Learning](https://arxiv.org/abs/2302.12238) by Nabeel Seedat, Alan Jeffares, Fergus Imrie and Mihaela van der Schaar (Cambridge, 2023) [Video](https://www.youtube.com/watch?v=pagxatn7xNw) [Code](https://github.com/seedatnabeel/SSCP)
191. [Learning by Transduction - of the of earliest conformal prediction papers] ([https://dl.acm.org/doi/10.5555/2074094.2074112#sec-comments](https://dl.acm.org/doi/10.5555/2074094.2074112#sec-comments)) by Alex Gammerman, Vladimir Vovk and Vladimir Vapnik (Royal Holloway, University of London, 1998) 🔥
192. [Hedging Predictions in Machine Learning](https://arxiv.org/pdf/cs/0611011.pdf) by Alexander Gammerman and Vladimir Vovk (2008) 🔥
193. [Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction](https://www.mdpi.com/2218-273X/8/3/85) by Ulf Norinder, Glenn Myatt and Ernst Ahlberg
194. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) TIME SERIES 🚀🔥
195. [Fortuna: A Library for Uncertainty Quantification in Deep Learning](https://arxiv.org/abs/2302.04019) by Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau (2023)
196. [Conformal inference is (almost) free for neural networks trained with early stopping](https://arxiv.org/abs/2301.11556) by Ziyi Liang, Yanfei Zhou, Matteo Sesia (2023)
197. [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://arxiv.org/pdf/2212.03710.pdf) by Mahmoud Shoush and Marlon Dumas (University of Tartu,2022)
198. [Machine-Learning Applications of Algorithmic Randomness](https://eprints.soton.ac.uk/258960/1/Random_ICML99.pdf) by Vladimir Vovk, Alex Gammerman and Craig Saunders (1999) 🔥
199. [On the universal distribution of the coverage in split conformal prediction](https://arxiv.org/abs/2303.02770) by Paulo C. Marques F. (2023)
200. [Lightweight, Uncertainty-Aware Conformalized Visual Odometry](https://arxiv.org/pdf/2303.02207.pdf) by Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula, and Amit Ranjan Trivedi (University of Illinois Chicago, 2023)
201. [Group conditional validity via multi-group learning](https://arxiv.org/abs/2303.03995v1) by Samuel Deng, Navid Ardeshir, Daniel Hsu (Columbia University, 2023)
202. [Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis](https://arxiv.org/pdf/2303.10694.pdf) by Subhankar Ghosh, Taha Belkhouj, Yan Yan, Janardhan Rao Doppa (Washington State University, 2023)
203. [Mondrian conformal regressors](https://proceedings.mlr.press/v128/bostrom20a.html) by Henrik Boström, Ulf Johansson (2020)
204. [Mondrian Conformal Predictive Distributions](https://proceedings.mlr.press/v152/bostrom21a/bostrom21a.pdf) by Henrik Boström, Ulf Johansson, Tuwe Löfström (2021) 🔥
205. [Adaptive Conformal Prediction by Reweighting Nonconformity Score](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou, Nicolas J.B Brunel (2023) [Code](https://github.com/salimamoukou/ACPI)
206. [Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation](https://arxiv.org/pdf/2303.12246.pdf) by Heng Yang and Marco Pavone (NVIDIA, 2023)
207. [A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum](https://arxiv.org/pdf/2010.07147.pdf) by Xiaoyu Hu and Jing Lei (Peking University and Carnegie Mellon University, 2023)
208. [Conformalized Semi-Supervised Random Forest For Classification and Abnormality Detection](https://arxiv.org/abs/2302.02237) (2023)
209. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023)
210. [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://arxiv.org/pdf/2304.00194.pdf) by Shuo Yang, George J. Pappas, Rahul Mangharam, and Lars Lindemann (University of Pennsylvania, 2023)
211. [Conformal Prediction Regions for Time Series using Linear Complementarity Programming](https://arxiv.org/pdf/2304.01075.pdf) by Matthew Cleaveland, Insup Lee, George J. Pappas†, and Lars Lindemann (University of Pennsylvania, 2023)
212. [Development and Evaluation of Conformal Prediction Methods for QSAR](https://arxiv.org/pdf/2304.00970.pdf) by Yuting Xua, Andy Liawa, Robert P. Sheridan (Merck, 2023)
213. [Multi-Agent Reachability Calibration with Conformal Prediction](https://arxiv.org/pdf/2304.00432v1.pdf) by Anish Muthali1, Haotian Shen, Sampada Deglurkar, Michael H. Lim, Rebecca Roelofs, Aleksandra Faust, Claire Tomlin (University of Berkeley, 2023)
214. [Conformalized Unconditional Quantile Regression](https://arxiv.org/pdf/2304.01426.pdf) by Ahmed M. Alaa, Zeshan Hussain, David Sontag (Berkeley/MIT, 2023).
215. [Conformal Off-Policy Evaluation in Markov Decision Processes](https://arxiv.org/pdf/2304.02574v1.pdf) by Daniele Foffano, Alessio Russo and Alexandre Proutiere (KTH, 2023) 🔥🌟
216. [Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning](https://arxiv.org/pdf/2304.03398.pdf) by Sangwoo Park and Osvaldo Simeone (2023) [Code](https://github.com/kclip/quantum-CP) QuantumML 🚀🔥
217. [Probabilistic prediction with locally weighted jackknife predictive system](https://link.springer.com/content/pdf/10.1007/s40747-023-01044-0.pdf#page18) by Di Wang, Ping Wang, Pingping Wang, Cong Wang, Zhen He, Wei Zhang 🔥
218. [Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision](https://arxiv.org/abs/2304.06158) by Siddhaarth Sarkar, Arun Kumar Kuchibhotla (2023)
219. [Conformal Regression in Calorie Prediction for Team Jumbo-Visma](https://arxiv.org/abs/2304.03778) by Kristian van Kuijk, Mark Dirksen, Christof Seiler (2023) 🔥[slides](https://copa-conference.com/presentations/Kristian.pdf)
220. [Design-based conformal prediction](https://arxiv.org/pdf/2303.01422.pdf) by Jerzy Wieczorek (2023) [code](https://github.com/ColbyStatSvyRsch/surveyConformal-paper-code)🌟
221. [Inductive Confidence Machines for Regression](https://link.springer.com/content/pdf/10.1007/3-540-36755-1_29.pdf) by Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman (2002) 🔥📚
222. [Model-Agnostic Nonconformity Functionsfor Conformal Classification](https://ieeexplore.ieee.org/document/7966105) by Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström (2017) 🔥📚
223. [Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study](https://proceedings.mlr.press/v152/aleksandrova21a.html) by Marharyta Aleksandrova, Oleg Chertov (2021) 🔥📚
224. [Inductive Conformal Prediciton: A Straightforward Introduction with examples in Python](https://arxiv.org/pdf/2206.11810.pdf) by Martim Sousa (2022) [Code](https://github.com/Quilograma/ConformalPredictionTutorial/blob/main/Conformal%20Prediction.ipynb) 🔥📚
225. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://stanfordasl.github.io/wp-content/papercite-data/pdf/Sinha.Pavone.CDC23.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Standord, 2023)
226. [Calibrated Explanations: with Uncertainty Information and Counterfactuals](https://arxiv.org/pdf/2305.02305.pdf) by Helena Löfström, Tuwe Löfström, Ulf Johansson, Cecilia S ̈onstr ̈od (2023) [Code](https://github.com/Moffran/calibrated_explanations) 🔥
227. [Optimizing Hyperparameters with Conformal Quantile Regression](https://arxiv.org/pdf/2305.03623.pdf) by David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau (Amazon Science, 2023) 🔥
228. [Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens](https://chemrxiv.org/engage/chemrxiv/article-details/6456778807c3f02937503688) by Andreas Luttens, Israel Cabeza de Vaca, Leonard Sparring, Ulf Norinder, Jens Carlsson (2023) [Code](https://github.com/carlssonlab/conformalpredictor) [Datasets](https://zenodo.org/record/7903161) 🔥
229. [Predicting skin sensitizers with confidence — Using conformal prediction to determine applicability domain of GARD](https://www.sciencedirect.com/science/article/pii/S0887233318300237?via%3Dihub) by Andy Forreryd, Ulf Norinder, Tim Lindberg, Malin Lindstedt (2018) 📚
230. [Confidence-based Prediction of Antibiotic Resistance at the Patient-level Using Transformers](https://www.biorxiv.org/content/10.1101/2023.05.09.539832v1.full.pdf) by J.S. Inda-Diaz, A. Johnning, M. Hessel, A. Sjo ̈berg, A. Lokrantz, L. Hellda, M. Jirstrand, L. Svensson and E. Kristiansson (Chalmers University of Technology and University of Gothenburg/Centre for Antibiotic Resistance Research (CARe), 2023) 🔥
231. [Framework based on conformal predictors and power martingales for detection of fixed football matches](https://www.semanticscholar.org/paper/Framework-based-on-conformal-predictors-and-power-Zhuk-Chertov/9405c6a70f5ce211afa575b4bdc4cddb449e9594?utm_source=alert_email&utm_content=AuthorCitation&utm_campaign=AlertEmails_DAILY&utm_term=LibraryFolder+AuthorCitation&email_index=1-0-3&utm_medium=15942999) by I. Zhuk, O. Chertov (2023)
232. [Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems](https://arxiv.org/pdf/2305.10124.pdf) by Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad (2023) 🔥
233. [Conformalized matrix completion](https://arxiv.org/pdf/2305.10637.pdf) by Yu Gui, Rina Foygel Barber, and Cong Ma (University of Chicago, 2023) 🔥 [code](https://github.com/yugjerry/conf-mc)
234. [Conformal Prediction With Conditional Guarantees](https://arxiv.org/pdf/2305.12616.pdf) by Isaac Gibbs, John Cherian, Emmanuel Candes (Stanford, 2023) [code](https://github.com/jjcherian/conditional-conformal) 🔥🔥🔥🔥🔥
235. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://arxiv.org/abs/2305.14535) by Kexin Huang, Ying Jin, Emmanuel Candes, Jure Leskovec (Stanford, 2023) [code](https://github.com/snap-stanford/conformalized-gnn) 🔥🔥🔥🔥🔥
236. [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://arxiv.org/pdf/2305.17564.pdf) by Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar (MIT/Berkeley, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) [video](https://www.youtube.com/watch?v=Ess8_S4avW4) 🔥🔥🔥🔥🔥
237. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/pdf/2305.18404.pdf) by Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (Harvard/MIT, 2023) 🔥🔥🔥🔥🔥
238. [Conformal Predictive Distribution Trees](https://link.springer.com/content/pdf/10.1007/s10472-023-09847-0.pdf) by Ulf Johansson, Tuwe Löfström, Henrik Boström (2023) 🔥🔥🔥🔥🔥
239. [CONFORMAL PREDICTION WITH PARTIALLY LABELED DATA](https://arxiv.org/pdf/2306.01191.pdf) by Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier (2023)
240. [Conformal Prediction for Federated Uncertainty Quantification Under Label Shift](https://arxiv.org/pdf/2306.05131.pdf) by Vincent Plassie, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov (2023)
241. [Conformalizing Machine Translation Evaluation](https://arxiv.org/pdf/2306.06221.pdfm) by Chrysoula Zerva, André F. T. Martins (2023)
242. [Class-Conditional Conformal Prediction With Many Classes](https://arxiv.org/pdf/2306.09335.pdf) by Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani 🔥🔥🔥🔥🔥 (Berkeley, 2023)
243. [Conformal Prediction Sets for Graph Neural Networks](https://openreview.net/pdf/1915b7fb247001d17114b5908727ac0f7056abe8.pdf) by Soroush Zargarbashi, Simone Antonelli, Aleksandar Bojchevski [Code](https://github.com/bhaweshiitk/ConformalLLM)
244. [Conformal link prediction to control the error rate](https://arxiv.org/pdf/2306.14693v1.pdf) by Ariane Marandon (2023)
245. [JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift](https://openreview.net/pdf?id=ORxBEWMPAJ) Drew Prinster, Suchi Saria, Anqi Liu (John Hopkins University, 2023) [Code](https://github.com/drewprinster/jaws-x) 🔥🔥🔥🔥🔥
246. [Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction](https://arxiv.org/pdf/2306.17815.pdf) by Yunchuan Zhang, Sangwoo Park and Osvaldo Simeone (2023)
247. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://arxiv.org/abs/2307.01928) by Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar (Princeton/DeepMind, 2023) [Website](https://robot-help.github.io/) 🔥🔥🔥🔥🔥
248. [Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery](https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-018-0325-4.pdf) by Nicolas Bosc, Francis Atkinson, Eloy Felix, Anna Gaulton, Anne Hersey and Andrew R. Leach (Cambridge, 2019) 🔥🔥🔥🔥🔥
249. [Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers](https://www.diva-portal.org/smash/get/diva2:1163353/FULLTEXT01.pdf) by Henrik Linusson, Ulf Johansson, Henrik Bostroem, and Tuve Loefstroem (2014)
250. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/pdf/2302.03791.pdf) [Code](https://github.com/Sulam-Group/k-rcps) (John Hopkins University, 2023) 🔥🔥🔥🔥🔥
251. [Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors](https://mdpi-res.com/d_attachment/applsci/applsci-13-08647/article_deploy/applsci-13-08647.pdf?version=1690443630) by Gang Wang, Zhiying Lu, Ping Wang, Shuo Zhuang and Di Wang (2023)
252. [Plug-in martingales for testing exchangeability on-line](https://icml.cc/2012/papers/808.pdf) by Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk (Royal Holloway, UK, ICML 2012) 🔥🔥🔥🔥🔥
253. [Testing Exchangeability On-Line](https://aaai.org/papers/100-testing-exchangeability-on-line/) by Vladimir Vovk, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, UK, ICML 2003) 🔥🔥🔥🔥🔥
254. [Predictive Inference Is Free with the Jackknife±after-Bootstrap](https://proceedings.neurips.cc/paper/2020/file/2b346a0aa375a07f5a90a344a61416c4-Paper.pdf) by Byol Kim, Chen Xu, Rina Foygel Barber (University of Chicago, 2020) 🔥🔥🔥🔥🔥
255. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/abs/2305.18404) by Bhawesh Kumar, Charles Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (MIT, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) 🔥🔥🔥🔥🔥
256. [CONFORMAL PREDICTIONS ENHANCED EXPERT-GUIDED MESHING WITH GRAPH NEURAL NETWORKS](https://arxiv.org/pdf/2308.07358.pdf) [code](https://github.com/ahnobari/AutoSurf) by Amin Heyrani Nobari, Justin Rey, Suhas Kodali and Matthew Jones (MIT, 2023) [website](https://decode.mit.edu/projects/autosurf/) 🔥🔥🔥🔥🔥
257. [Approximating Full Conformal Prediction at Scale via Influence Functions](https://arxiv.org/pdf/2202.01315.pdf) by Javier Abad, Umang Bhatt, Adrian Weller, Giovanni Cherubin (Cambridge, Alan Turing Institute, ETH, Microsoft Research, 2023) [code](https://github.com/cambridge-mlg/acp) [video](https://www.youtube.com/watch?v=LRwm976poDE) 🔥🔥🔥🔥🔥
258. [Robust Uncertainty Quantification using Conformalised Monte Carlo Prediction](https://arxiv.org/pdf/2308.09647.pdf) [Code](https://github.com/team-daniel/MC-CP) by Daniel Bethell, Simos Gerasimou, Radu Calinescu (University of York, 2023) 🔥🔥🔥🔥🔥
259. [I do not know! but why?”– Local Model-Agnostic Example-based explanations of reject](https://www.sciencedirect.com/science/article/pii/S0925231223008457) [code](https://github.com/HammerLabML/LocalModelAgnosticExamplebasedExplanationsReject) by Andre Artelt, Roel Visser and Barbara Hammer (University of Bielefeld, 2023)
260. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://arxiv.org/pdf/2308.11975.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir and Ulf Johansson (KTH/Joenkoeping University, 2023) 🔥🔥🔥🔥🔥
261. [Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions](https://arxiv.org/pdf/2212.13629.pdf) by Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel (Princeton University, Columbia University, Harvard University, University of Toronto, 2023) 🔥🔥🔥🔥🔥
262. [Improving Deep Learning-Based Defect Classification in Solar Cells using Conformal Prediction](https://backend.orbit.dtu.dk/ws/portalfiles/portal/333872349/FinalManuscript_2022_714_0602131208.pdf) by Vitus Bødker Thomsen, Claire Mantel, Gisele Benatto, Søren Forchhammer (DTU - Technical University of Denmark, 2023) 🔥🔥🔥🔥🔥
263. [Adaptive Conformal Prediction by Reweighting Nonconformity Scores](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou and Nicolas J-B. Brunel and Nicolas J-B. Brunel (University Paris Saclay, Stellantis Paris, Quantmetry Paris, 2023) [code](https://github.com/salimamoukou/ACPI) 🔥🔥🔥🔥🔥
264. [Copula-based conformal prediction for Multi-Target Regression](https://arxiv.org/abs/2101.12002) by Soundouss Messoudi, Sébastien Destercke, Sylvain Rousseau (2021) [code](https://github.com/M-Soundouss/CopulaConformalMTR) 🔥🔥🔥🔥🔥
265. [Conformal Meta-learners for Predictive Inference of Individual Treatment Effects](https://arxiv.org/abs/2308.14895) by Ahmed Alaa, Zaid Ahmad, and Mark van der Laan (2023) [code](https://github.com/AlaaLab/conformal-metalearners) 🔥🔥🔥🔥🔥
266. [Adaptive conformal classification with noisy labels](https://arxiv.org/pdf/2309.05092.pdf) by Matteo Sesia, Y. X. Rachel Wang†, Xin Tong [code](https://github.com/msesia/conformal-label-noise) 🔥🔥🔥🔥🔥
267. [Heteroskedastic conformal regression](https://arxiv.org/pdf/2309.08313.pdf) by NICOLAS DEWOLF, BERNARD DE BAETS AND WILLEM WAEGEMAN (2023) 🔥🔥🔥🔥🔥 (UGent, 2023)
268. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://arxiv.org/pdf/2309.08603.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Stanford, 2023) 🔥🔥🔥🔥🔥
269. [Conformal Temporal Logic Planning using Large Language Models: Knowing When to Do What and When to Ask for Help](https://arxiv.org/abs/2309.10092) [project website](https://ltl-llm.github.io/) by Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros (Washington University in St.Louis and University of Zurich, 2023) 🔥🔥🔥🔥🔥
270. [Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method](https://www.nature.com/articles/s41598-022-17609-x) [code](https://github.com/unisb-bioinf/Conformal-Drug-Sensitivity-Prediction) by Kerstin Lenhof, Lea Eckhart, Nico Gerstner, Tim Kehl & Hans-Peter Lenhof (Saarland University, 2022) 🔥🔥🔥🔥🔥
271. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candès (Stanford, 2019) [code](Conformalized Quantile Regression]([https://github.com/yromano/cqr](https://github.com/yromano/cqr)) [project](https://sites.google.com/view/cqr) 🔥🔥🔥🔥🔥
272. [Fast nonlinear vector quantile regression](https://arxiv.org/pdf/2205.14977.pdf) by Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano and Alex M. Bronstein (Technion, 2023) [code](https://github.com/vistalab-technion/vqr) 🔥🔥🔥🔥🔥
273. [Testing for Outliers with Conformal p-values](https://arxiv.org/abs/2104.08279) by Stephen Bates, Emmanuel Candes,Lihua Lei, Yaniv Romano,Matteo Sesia (Berkeley/Stanford/Technion, 2022) [code](https://github.com/msesia/conditional-conformal-pvalues) 🔥🔥🔥🔥🔥
274. [Achieving Risk Control in Online Learning Settings](https://arxiv.org/abs/2205.09095) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (Technion/Berkeley, 2021) [code](https://github.com/Shai128/rrc)
275. [Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ell2.12860) by Lorin Werthen-Brabants, Tom Dhaene, Dirk Deschrijver (Ghent University, 2023) 🔥🔥🔥🔥🔥
276. [Online NoVaS Conformal Volatility Prediction](https://proceedings.mlr.press/v204/canete23b/canete23b.pdf) [slides](https://copa-conference.com/presentations/3%20-%20COPA-2023.NoVas.v005.pptx) by Alejandro Canete (University of Chicago) 🔥🔥🔥🔥🔥 (2023)
277. [Market Implied Conformal Volatility Intervals](https://proceedings.mlr.press/v204/canete23a/canete23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023.ImpliedIntervals.v005.pptx) by Alejandro Canete (University of Chicago) 🔥🔥🔥🔥🔥 (2023)
278. [PUNCC: a Python Library for Predictive Uncertainty Calibration and Conformalization](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) [code](https://github.com/deel-ai/puncc) 🔥🔥🔥🔥🔥
279. [The Venn-ABERS Testing for Change-Point Detection](https://proceedings.mlr.press/v204/nouretdinov23b/nouretdinov23b.pdf) [slides](https://copa-conference.com/presentations/Nouretdinov_poster_slides.pdf) by Ilia Nouretdinov and Alex Gammerman (Royal Holloway, 2023) 🔥🔥🔥🔥🔥
280. [A Review of Nonconformity Measures for Conformal Prediction in Regression](https://proceedings.mlr.press/v204/kato23a/kato23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023%20-%20v2.pptx) by Yuko Kato, David M.J. Tax, Marco Loog (Delft University of Technology, Radboud University, Nijmegen, Netherlands, 2023)
281. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://proceedings.mlr.press/v204/alkhatib23a/alkhatib23a.pdf) [slides](https://copa-conference.com/presentations/Amr-Alkhatib.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir, Ulf Johansson (KTH, Joenkoeping University, 2023).
282. [Mondrian Predictive Systems for Censored Data](https://proceedings.mlr.press/v204/bostrom23a/bostrom23a.pdf) [slides](https://copa-conference.com/presentations/Bostrom.pdf) by Henrik Bostroem, Henrik Linusson, Anders Vesterberg (KTH, Ekkono Solutions AB, Scania CV AB, Sweden 2023).
283. [Evaluating Machine Translation Quality with Conformal Predictive Distributions](https://proceedings.mlr.press/v204/giovannotti23a/giovannotti23a.pdf) [slides](https://copa-conference.com/presentations/MTpres.pdf) by Patrizio Giovannotti (Royal Holloway, UK) 🔥🔥🔥🔥🔥
284. [Confidence Calibration for Systems with Cascaded Predictive Modules](https://arxiv.org/pdf/2309.12510.pdf) by Yunye Gong, Yi Yao, Xiao Lin, Ajay Divakaran, Melinda Gervasio (SRI International, 2023)
285. [TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images](https://arxiv.org/pdf/2307.15638.pdf) by Benjamin Lambert, Florence Forbes, Senan Doyle, and Michel Dojat (Grenoble Institut Neurosciences / Univ. Grenoble Alpes, Pixyl, 2023) 🔥🔥🔥🔥🔥
286. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
287. [Distribution-free risk assessment of regression-based machine learning algorithms](https://arxiv.org/pdf/2310.03545.pdf) by Sukrita Singh, Neeraj Sarna, Yuanyuan Li, Yang Lin, Agni Orfanoudaki (University of Oxford / MunichRe, 2023).
288. [CONFORMAL PREDICTION FOR DEEP CLASSIFIER VIA LABEL RANKING](https://arxiv.org/pdf/2310.06430.pdf) by Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei (Southern University of Science and Technology, ShanghaiTech University, Rutgers University, University of North Carolina at Chapel Hill, Chongqing University) 🔥🔥🔥🔥🔥 [code](https://github.com/ml-stat-Sustech/conformal_prediction_via_label_ranking)
289. [On the Expected Size of Conformal Prediction Sets](https://arxiv.org/abs/2306.07254) by Guneet S. Dhillon, George Deligiannidis, Tom Rainforth (University of Oxford, 2023) [code](https://github.com/Guneet-Dhillon/expected-conformal-prediction-set-size)🔥🔥🔥🔥🔥
290. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)🔥🔥🔥🔥🔥
291. [Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification](https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Melki_Group-Conditional_Conformal_Prediction_via_Quantile_Regression_Calibration_for_Crop_and_ICCVW_2023_paper.html) by Paul Melki, Lionel Bombrun, Boubacar Diallo, Jérôme Dias, Jean-Pierre Da Costa by EXXACT Robotics (2023) 🔥🔥🔥🔥🔥
292. [Guaranteed Coverage Prediction Intervals with Gaussian Process Regression](https://arxiv.org/abs/2310.15641) by Harris Papadopoulos (2023) 🔥🔥🔥🔥🔥
293. [Conformal prediction with missing values](https://arxiv.org/abs/2306.02732) by Margaux Zaffran, Aymeric Dieuleveut, Julie Joss, Yaniv Romano (2023) [Video](https://icml.cc/virtual/2023/poster/23530) [summary](https://mzaffran.github.io/assets/files/Posters/cp_na_summary.pdf) [code](https://github.com/mzaffran/ConformalPredictionMissingValues) 🔥🔥🔥🔥🔥
294. [TRIAGE: Characterizing and auditing training data for improved regression](https://arxiv.org/pdf/2310.18970.pdf) by Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar (University of Cambridge, 2023) [code](https://github.com/seedatnabeel/TRIAGE)) 🔥🔥🔥🔥🔥
295. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
296. [Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model](https://openreview.net/pdf?id=VeO03T59Sh) by Jiankai Sun, Jiankai\_Sun, Yiqi Jiang, Jianing Qiu, Parth Talpur Nobel, Mykel Kochenderfer, Mac Schwager (Staford, Imperial College, 2023)
297. [CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift](https://openreview.net/pdf?id=GgdFLb94Ld) by Siddhartha Laghuvarapu, Zhen Lin, Jimeng Sun (University of Illinois Urbana-Champaign, 2023)
298. [PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction](https://openreview.net/forum?id=URrUpcp6Qh) Apoorva Sharma, Sushant Veer, Asher Hancock, Heng Yang, Marco Pavone, Anirudha Majumdar (NVIDIA Research, Harvard, Princeton, Stanford 2023) 🔥🔥🔥🔥🔥
299. [Conformal Prediction Sets for Ordinal Classification](https://openreview.net/pdf?id=YI4bn6aAmz) by Prasenjit Dey, Srujana Merugu, Sivaramakrishnan Kaveri (Amazon, 2023) 🔥🔥🔥🔥🔥
300. [Beyond Confidence: Reliable Models Should Also Consider Atypicality](https://openreview.net/pdf?id=xDCmlkSavR) by Mert Yuksekgonul, Linjun Zhang, James Zou, Carlos Guestrin (Stanford/Rutgers) [code](https://github.com/mertyg/beyond-confidence-atypicality) 🔥🔥🔥🔥🔥
301. [On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning](https://arxiv.org/abs/2311.12688) by Paul Scemama, Ariel Kapusta (The Mitre Corporation, 2023)
302. [A powerful rank-based correction to multiple testing under positive dependency](https://arxiv.org/pdf/2311.10900.pdf) by Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick (Bosch AI, 2023)
303. [Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models](https://arxiv.org/pdf/2311.13628.pdf) by Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel (Columbia University, 2023) 🔥🔥🔥🔥🔥
304. [Risk-Aware and Explainable Framework for Ensuring Guaranteed Coverage in Evolving Hardware Trojan Detection](https://arxiv.org/abs/2312.00009) by Rahul Vishwakarma, Amin Rezaei (California State University, 2023) 🔥🔥🔥🔥🔥
305. [Uncertainty quantification in automated valuation models with locally weighted conformal prediction](https://arxiv.org/abs/2312.06531) by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams (University of Oslo, North Carolina State University, 2023) 🔥🔥🔥🔥🔥
306. [Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates](https://arxiv.org/abs/2312.07434) by Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann [code](https://github.com/nandantumu/conformal_region_designer) (University of Pennsylvania, 2023) 🔥🔥🔥🔥🔥
307. [Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction](https://arxiv.org/abs/2312.08604) by Albert Lin, Somil Bansal (University of Southern California, 2023) 🔥🔥🔥🔥🔥
308. [Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms](https://arxiv.org/abs/2311.11837) [code](https://github.com/NKI-AI/kandinsky-calibration) by Joren Brunekreef, Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen (The Netherlands Cancer Institute, University of Amsterdam) (2023)
309. [Forecasting CPI inflation under economic policy and geo-political uncertainties](https://arxiv.org/abs/2401.00249) by Shovon Sengupta, Tanujit Chakraborty, Sunny Kumar Singh (Fidelity Investments, Sorbonne University, BITS Pilani Hyderabad). (2023) 🔥🔥🔥🔥🔥
310. [Conformal prediction of option prices](https://www.repository.utl.pt/handle/10400.5/29690) by Bastas Joao (University of Lisbon, 2023) 🔥🔥🔥🔥🔥
311. [Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction](https://arxiv.org/abs/2401.01549) by Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, Mengdi Huai (Iowa University, Pennsylvania State University, Georgia Institute of Technology (2024).
312. [Greek Proverbs: Computational Spatial Attribution](https://www.researchsquare.com/article/rs-3360387/v2) by John Pavlopoulos and Panos Louridas, Athens University of Economics and Business, Greece (2024)
313. [Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations](https://arxiv.org/pdf/2401.01977.pdf) by Bingkai Wang, Fan Li and Mengxin (University of Michigan, Yale, University of Pennsylvania)
314. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

## Papers Time Series

1. [Conformal prediction interval for dynamic time-series](https://proceedings.mlr.press/v139/xu21h.html) by Chen Xu, Yao Xie (Georgia Tech, 2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥 [Python Code](https://github.com/hamrel-cxu/EnbPI) [Video](https://vimeo.com/583595474) [Video ICML2021](https://papertalk.org/papertalks/32044)
2. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) [Python Code](https://github.com/hamrel-cxu/Ensemble-Regularized-Adaptive-Prediction-Set-ERAPS) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
3. [Conformal Time-Series Forecasting](https://proceedings.neurips.cc/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf) by Kamile Stankeviciu te and Ahmed M. Alaa (2021) [Video NeurIPS2021](https://papertalk.org/papertalks/36577) [Video](https://www.youtube.com/watch?v=xmPDk4QRAAI) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
4. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2202.08756.pdf) by Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen (2022). TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
5. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) [Python Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥 [Video](https://www.youtube.com/watch?v=Yuxu9aUpVi0)
6. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://arxiv.org/pdf/2205.09095.pdf) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🔥🔥🔥🔥🔥 [Code](https://github.com/Shai128/rci)
7. [Conformal Prediction with Temporal Quantile Adjustments](https://arxiv.org/pdf/2205.09940.pdf) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
8. [Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data](http://proceedings.mlr.press/v75/chernozhukov18a/chernozhukov18a.pdf) by Victor Chernozhukov (MIT), Kaspar Wuethrich (University of California, San Diego) and Yinchu Zhu (University of Oregon) (2018) [Video](https://www.youtube.com/watch?v=Wcm9Uw0YL8A&t=9s)
9. [Distributional Conformal Prediction](https://www.pnas.org/doi/pdf/10.1073/pnas.2107794118) by Chernozhukov (MIT), Kaspar Wuethrich (University of California, San Diego) and Yinchu Zhu (University of Oregon) (2022)
10. [Distribution-Free Prediction Bands for Multivariate Functional Time Series: an Application to the Italian Gas Market](https://arxiv.org/abs/2107.00527) by Jacopo Diquigiovanni (University of Padua) Matteo Fontana (Joint Research Centre - European Commission) Simone Vantini (Politecnico di Milano) (2021)
11. [CODiT: Conformal Out-of-Distribution Detection in Time- Series Data](https://arxiv.org/pdf/2207.11769.pdf) by Ramneet Kaur et.al., Unibersity of Pensylvania (2022). [Code](https://github.com/kaustubhsridhar/time-series-OOD) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
12. [A General Framework For Multi-step Ahead Adaptive Conformal Heteroscedastic Time Series Forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tomé, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
13. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath and Florian Ziel (2020)
14. [Robust Gas Demand Forecasting With Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
15. [Conformal Prediction Bands for Two-Dimensional Functional Time Series](https://arxiv.org/pdf/2207.13656.pdf) by Niccolo` Ajroldia, Jacopo Diquigiovannib, Matteo Fontanac, Simone Vantinia (2022)
16. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
17. [Conformal prediction set for time-series](Conformal prediction set for time-series) by Chen Xu, Yao Xie (Georgia Tech, 2022) [Code](https://github.com/hamrel-cxu/Ensemble-Regularized-Adaptive-Prediction-Set-ERAPS)
18. [Amazon Fortuna](https://aws-fortuna.readthedocs.io/en/latest/examples/enbpi_ts_regression.html)
19. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) [code](https://github.com/salesforce/online_conformal) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
20. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://arxiv.org/pdf/2303.12783.pdf) by Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter (Johannes Kepler University, Linz, 2023) code [blogpost](https://ml-jku.github.io/HopCPT/) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
21. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/abs/2212.03463) by Chen Xu, Yao Xie (Georgia Tech) [code](https://github.com/hamrel-cxu/SPCI-code/tree/main) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
22. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
23. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
24. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

## Presentation Slides

1. [Machine Learning for Probabilistic Prediction, Seattle Artificial Intelligence Workshops Meetup](https://www.researchgate.net/publication/371566526_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin, 2023 🔥🔥🔥🔥🔥
2. [Machine Learning for Probabilistic Prediction](https://github.com/valeman/awesome-conformal-prediction/blob/main/Machine%20Learning%20for%20Probabilistic%20Prediction.pdf) by Valery Manokhin, 2022 🔥🔥🔥🔥🔥
3. [Adaptive Conformal Anomaly Detection for Time-series](https://cml.rhul.ac.uk/copa2017/presentations/burnaev.pdf) by Evgeny Burnaev, Alexander Bernstein, Vlad Ishimtsev and Ivan Nazarov (Skoltech, Moscow, Russia, 2017)
4. [Nonparametric predictive distributions based on conformal prediction](https://cml.rhul.ac.uk/copa2017/presentations/vovk.pdf) by Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, University of London Rutgers University, 2017)
5. [What Can Conformal Inference Offer to Statistics?](https://lihualei71.github.io/Job_Talk_Lihua_Lei.pdf) by Lihua Lei, Stanford University
6. [Conformal Regressors and Predictive Systems – a Gentle Introduction](https://copa-conference.com/presentations/COPA_2022_Presentation __Conformal_Regressors_and_Predictive_Systems___ a_Gentle_Introduction.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
7. [Applications of Conformal Predictors](https://copa-conference.com/presentations/lars_ernst.pdf) by Ernst Ahlberg and Lars Carlsson (Stena Line, 2022)
8. [crepes: a Python Package for Conformal Regressors and Predictive Systems](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
9. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/presentations/amr.pdf) by Amr Alkhatib, Henrik Boström and Ulf Johansson (2022)
10. [Well-Calibrated Rule Extractors](https://copa-conference.com/presentations/Johansson_U_COPA2022_pres.pdf) by Ulf Johansson, Tuwe Löfström, Niclas Ståhl (2022)
11. [Calibration of Natural Language Understanding Models with Venn-ABERS Predictors](Calibration of Natural Language Understanding Models with Venn-ABERS Predictors]([https://copa-conference.com/presentations/patrizio.pdf](https://copa-conference.com/presentations/patrizio.pdf)) by Patrizio Giovannotti (2022)
12. [Reinforcement Learning Prediction Intervals with Guaranteed Fidelity](https://web.engr.oregonstate.edu/~tgd/talks/dietterich-caml-rl-prediction-intervals.pdf) by Thomas Dietterich (University of Oregon, 2022)
13. [Conformal Prediction beyond exchangeability](https://copa-conference.com/presentations/conformal_UAI_tutorial_2.pdf) by Rina Foygel Barber (University of Chicago, 2022) 🔥🔥🔥🔥🔥
14. [Split conformal prediction for dependant data](http://www.im.ufrj.br/~coloquiomea/apresentacoes/orenstein_2022.pdf) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos and João Vitor Romano (2022)
15. [Conformal prediction of small-molecule drug resistance in cancer cell lines](https://copa-conference.com/presentations/saiveth.pdf) by Saiveth Hernandez-Hernandez, Sachin Vishwakarma and Pedro Ballester
16. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/pdf/2212.03463.pdf) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
17. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://arxiv.org/abs/2212.03281) by Sophia Sun, Rose Yu (University of California, San Diego, 2022)
18. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, 2022)
19. [Conformal Prediction: an Introduction](https://github.com/online-ml/river/files/10098389/Conformal_Prediction_Presentation.pdf) by Leo Andeol (2022)
20. [Adaptive Conformal Predictions for Time Series](https://github.com/valeman/awesome-conformal-prediction/blob/main/Adaptive%20Conformal%20Predictions%20for%20Time%20Series.pdf) by Margaux Zaffran (ICML,2021)
21. [EnbPI poster](https://github.com/valeman/awesome-conformal-prediction/blob/main/EnbPI_poster.pdf) by Chen Xu, Yao Xie (2021)
22. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/370012303_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin (2023) 🔥🔥🔥🔥🔥
23. [Conformal Inference with Tidymodels](https://topepo.github.io/posit-2023-conformal/#/title-slide) by Max Kuhn (posit conference 2023) 🔥🔥🔥🔥🔥
24. [Graceful handling of large imbalanced datasets using Conformal Prediction](https://bigchem.eu/sites/default/files/AZ_school_Norinder_public.pdf) by Ulf Norinder and Fredrik Svensson 🔥🔥🔥🔥🔥
25. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candès (Stanford, 2019)
26. [Leveraging Conformal Prediction for Calibrated Probabilistic Time Series Forecasts to Accelerate the Renewable Energy Transition](https://speakerdeck.com/ingevandenende/leveraging-conformal-prediction-for-calibrated-probabilistic-time-series-forecasts-to-accelerate-the-renewable-energy-transition) by Inge van den Ende (Dexter Energy, 2023). TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

## Researchers

1. [Vladimir Vovk](https://github.com/valeman/awesome-conformal-prediction/blob/main/vovk.net), Royal Holloway, United Kingdom
2. [Alexander Gammerman](https://cml.rhul.ac.uk/people/alex/), Royal Holloway, United Kingdom
3. [Glenn Shafer](http://www.glennshafer.com/), Rutgers University, USA
4. [Emmanuel Candès](https://candes.su.domains/), Stanford, USA
5. [Ryan Tibshiriani](https://www.stat.cmu.edu/~ryantibs/), Carnegie Mellon, USA
6. [Yaniv Romano](https://sites.google.com/view/yaniv-romano/), Technion—Israel Institute of Technology
7. [Michael I. Jordan](https://people.eecs.berkeley.edu/~jordan/), Berkeley, USA
8. [Jitendra Malik](https://people.eecs.berkeley.edu/~malik/), Berkeley, USA
9. [Anastasios Angelopoulos](https://people.eecs.berkeley.edu/~angelopoulos/), Berkeley, USA
10. [Lihua Lei](https://lihualei71.github.io/), Stanford, USA
11. [Henrik Boström](https://www.kth.se/profile/henbos), KTH, Sweden
12. [Ulf Johansson](https://scholar.google.com/citations?user=OZjCgIsAAAAJ&hl=en), Jönköping University, Sweden
13. [Henrik Linusson](https://scholar.google.se/citations?user=Xl8W39cAAAAJ&hl=en), University of Borås, Sweden
14. [Harris Papadopoulos](http://staff.fit.ac.cy/com.ph/), Frederick University, Cyprus
15. [Vladimir V’yugin](http://iitp.ru/ru/users/125.htm), Institute for Information Transmission Problems (IITP), Russia
16. [Evgeny Burnaev](https://faculty.skoltech.ru/people/evgenyburnaev), Skoltech, Russia
17. [Aaditya Ramdas](http://stat.cmu.edu/~aramdas/), Carnegie Mellon, USA
18. [Benjamin LeRoy](https://benjaminleroy.github.io/), Carnegie Mellon, USA
19. [Victor Chernozhukov](https://www.mit.edu/~vchern/), MIT, USA
20. [Ulf Norinder](https://scholar.google.com/citations?user=i5hUEFwAAAAJ&hl=en), Stockholm University, Sweden
21. [Ola Spjuth](https://pharmb.io/), Uppsala University, Sweden
22. [Ilia Nouretdinov](https://cml.rhul.ac.uk/people/nouretdinov/index.htm), Royal Holloway, United Kingdom
23. [Matteo Fontana](https://scholar.google.com/citations?user=U7jODH8AAAAJ&hl=it), Royal Holloway, University of London, United Kingdom
24. [Yao Xie](https://www2.isye.gatech.edu/~yxie77/), Georgia Institute of Technology
25. [Zhimeo Ren](https://zhimeir.github.io/), University of Chicago
26. [Rafael Izbicki](http://www.rizbicki.ufscar.br/), Federal University of São Carlos (UFSCar) Brazil
27. [Rina Foygel Barber](https://rinafb.github.io/) University of Chicago
28. [Matteo Sesia](https://msesia.github.io/) University of Southern California, Marshall School of Business
29. [Simone Vantini](https://scholar.google.it/citations?user=Cy2mv3YAAAAJ&hl=it) MOX - Department of Mathematics, Politecnico di Milano

## Articles

1. [Measuring Models’ Uncertainty: Conformal Prediction](https://blog.dataiku.com/measuring-models-uncertainty-conformal-prediction) by Leo Dreyfus-Schmidt (Dataiku, 2020). 🔥🔥🔥🔥🔥
2. [Conformal Prediction for Neural Regression Model](https://pkghosh.wordpress.com/2021/12/30/conformal-prediction-for-a-neural-regression-model/) by Pranab Ghosh (2021). 🔥🔥🔥🔥🔥
3. [How to Handle Uncertainty in Forecasts](https://towardsdatascience.com/how-to-handle-uncertainty-in-forecasts-86817f21bb54) by Michael Berk (2021)
4. [How to Add Uncertainty Estimation to your Models with Conformal Prediction](https://towardsdatascience.com/how-to-add-uncertainty-estimation-to-your-models-with-conformal-prediction-a5acdb86ea05) by Zachary Warnes (2021)
5. [nonconformist: An easy way to estimate prediction intervals](https://medium.com/spikelab/nonconformist-an-easy-way-to-estimate-prediction-intervals-b0ded1eb066f) by Maria Jesus Ugarte (2021).
6. [Detecting Weird Data: Conformal Anomaly Detection](https://towardsdatascience.com/detecting-weird-data-conformal-anomaly-detection-20afb36c7bcd) by Matthew Burruss (2020).
7. [“MAPIE” Explained Exactly How You Wished Someone Explained to You](https://towardsdatascience.com/mapie-explained-exactly-how-you-wished-someone-explained-to-you-78fb8ce81ff3) by Samuele Mazzanti (2022). 🔥🔥🔥🔥🔥
8. [With MAPIE, uncertainties are back in machine learning](https://towardsdatascience.com/with-mapie-uncertainties-are-back-in-machine-learning-882d5c17fdc3) by Vianney Taquet (2021) 🔥🔥🔥🔥🔥
9. [How to Predict Risk-Proportional Intervals with Conformal Quantile Regression](https://towardsdatascience.com/how-to-predict-risk-proportional-intervals-with-conformal-quantile-regression-175775840dc4) by Samuele Mazzanti (2022). 🔥🔥🔥🔥🔥
10. [Stanford statisticians and Washington Post data scientists build more honest prediction models](https://news.stanford.edu/2021/03/19/honesty-statistical-models/#) Stanford (2021) 🔥🔥🔥🔥🔥
11. [How to Detect Anomalies — state-of-the-art methods using Conformal Prediction](https://valeman.medium.com/how-to-detect-anomalies-state-of-the-art-methods-using-conformal-prediction-e02691659412) by Valery Manokhin (2021) 🔥🔥🔥🔥🔥
12. [How to calibrate your classifier in an intelligent way](https://medium.com/@valeman/how-to-calibrate-your-classifier-in-an-intelligent-way-a996a2faf718) by Valery Manokhin (2022) 🔥🔥🔥🔥🔥
13. [Conformal Prediction forecasting with MAPIE](https://medium.com/@valeman/conformal-prediction-forecasting-with-mapie-library-for-conformal-prediction-7aac033ae3ef) by Valery Manokhin (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
14. [How to predict full probability distribution using machine learning Conformal Predictive Distributions](https://medium.com/@valeman/how-to-predict-full-probability-distribution-using-machine-learning-conformal-predictive-f8f4d805e420) by Valery Manokhin (2022) 🔥🔥🔥🔥🔥
15. [How to predict quantiles in a more intelligent way (or ‘Bye-bye quantile regression, hello Conformal Quantile Regression](https://valeman.medium.com/how-to-predict-quantiles-in-a-more-intelligent-way-or-bye-bye-quantile-regression-hello-24a65e4c50f) by Valery Manokhin (2022) 🔥🔥🔥🔥🔥
16. [Conformal Prediction in Julia, Part I - Introduction](https://towardsdatascience.com/conformal-prediction-in-julia-351b81309e30) by Patrick Altmeyer (2022)
17. [Getting predictions intervals with conformal inference](http://projects.rajivshah.com/blog/2022/09/24/conformal_predictions/) by Rajiv Shah (2022)
18. [How to Conformalize a Deep Image Classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) by Patrick Altmeyer (2022)
19. [Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need](https://towardsdatascience.com/time-series-forecasting-with-conformal-prediction-intervals-scikit-learn-is-all-you-need-4b68143a027a) by Marco Cerliani (2022)
20. [Conformal Prediction in Julia, Part II - How to conformalize a deep image classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) by Patrick Altmeyer (2022)
21. [Conformal Prediction in Julia, Part III - Prediction intervals for any regression model](https://towardsdatascience.com/prediction-intervals-for-any-regression-model-306930d5ad9a) by Patrick Altmeyer (2022)
22. [Probabilistic Forecasting with Conformal Prediction and NeuralProphet](https://medium.com/@valeman/probabilistic-forecasting-with-conformal-prediction-and-neuralprophet-af9c87901d94) by Valery Manokhin (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
23. [Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need](https://towardsdatascience.com/time-series-forecasting-with-conformal-prediction-intervals-scikit-learn-is-all-you-need-4b68143a027a) by Marco Cerliani (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
24. [TQA: Creating Valid Prediction Intervals for Cross-sectional Time Series Regression](https://realsunlab.medium.com/tqa-creating-valid-prediction-intervals-for-cross-sectional-time-series-regression-bd0f2260fae7) by Zhen Lin (UIUC, NeurIPS’22 paper) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
25. [Conformal Prediction: A Critic to Predictive Models](https://medium.com/@data-overload/conformal-prediction-a-critic-to-predictive-models-27501dcc76d4) (2023)
26. [Multi-horizon Probabilistic Forecasting with Conformal Prediction and NeuralProphet](https://valeman.medium.com/multi-horizon-probabilistic-forecasting-with-conformal-prediction-and-neuralprophet-5ec5af3888c8) by Valery Manokhin TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
27. [Putting clear bounds on uncertainty](https://news.mit.edu/2023/putting-clear-bounds-uncertainty-0123) (MIT, 2023)
28. [Conformal prediction theory explained](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-theory-explained-14a35226df80) by Artem Ryasik (2023)
29. [Easy Distribution-Free Conformal Intervals for Time Series](https://towardsdatascience.com/easy-distribution-free-conformal-intervals-for-time-series-665137e4d907) by Michael Keith (2023)
30. [Another (Conformal) Way to Predict Probability Distributions](https://towardsdatascience.com/another-conformal-way-to-predict-probability-distributions-fcc63e78680d) by Harrison Hoffman (2023) 🔥🔥🔥🔥🔥
31. [How to use full (transductive) Conformal Prediction])([https://valeman.medium.com/how-to-use-full-transductive-conformal-prediction-7ed54dc6b72b](https://valeman.medium.com/how-to-use-full-transductive-conformal-prediction-7ed54dc6b72b)) by Valery Manokhin (2023) 🔥🔥🔥🔥🔥
32. [Conformal Prediction for Regression](https://medium.com/@artem_ryasik/conformal-prediction-for-regression-1ba24f1442df) (using KNIME) by Artem Ryasik (2023)
33. [Dynamic Conformal Intervals for any Time Series Model](https://towardsdatascience.com/dynamic-conformal-intervals-for-any-time-series-model-d1638aa48527) by Michael Keith (2023) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
34. [Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting](https://towardsdatascience.com/hitting-time-forecasting-the-other-way-for-time-series-probabilistic-forecasting-6c3b6496c353) by Marco Cerliani (2023) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
35. [Series of Medium articles about Conformal Prediciton (in Portuguese)](https://github.com/gusbruschi13/Conformal-Prediction) by Gustavo Bruschi (2023) 🔥🔥🔥🔥🔥
36. [Stanford statisticians and Washington Post data scientists build more honest prediction models](https://news.stanford.edu/2021/03/19/honesty-statistical-models/) [Code](https://github.com/washingtonpost/elex-live-model) 🔥🔥🔥🔥🔥
37. [Jackknife+ — a Swiss knife of Conformal Prediction for regression](https://medium.com/@valeman/jackknife-a-swiss-knife-of-conformal-prediction-for-regression-ce3b56432f4f) by Valeriy Manokhin (2023) 🔥🔥🔥🔥🔥
38. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)🔥🔥🔥🔥🔥
39. [How to use Conformal Prediction](https://kiel.ai/conformal-prediction/) by Yannick Kälber (2023) 🔥🔥🔥🔥🔥
40. [Model Diagnostics: Prediction Uncertainty](https://piml.medium.com/model-diagnostics-prediction-uncertainty-990ff0bd2c4e) by PiML team, Wells Fargo (2023).
41. [Make any predictor uncertainty-aware via conformal prediction](https://mlwithouttears.com/2023/11/15/make-any-predictor-uncertainty-aware-via-conformal-prediction/) by Lorenzo Maggi (2023)
42. [Conformal predictive systems - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-predictive-systems-895c40bac2ca) by Artem Ryasik (2023) 🔥🔥🔥🔥🔥
43. [Conformal prediction for classification - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-for-classification-6bf152abb491) by Artem Ryasik (2023) 🔥🔥🔥🔥🔥

## Kaggle

1. [Kaggle Notebook showcasing Conformal Predictive Distributions on Playground Series Season 3, Episode 1 (California Housing data) competition](https://www.kaggle.com/code/predaddict/conformal-predictive-distributions-pss3-e1) by Valeriy Manokhin (2022)
2. [Kaggle Notebook showcasing Venn-ABERs Conformal Prediction on Playground Series Season 3, Episode 2 (Stroke prediction) competition](https://www.kaggle.com/predaddict/pss-3-episode-2-jan-2023-stroke-prediction/)
3. [Regression prediction intervals with MAPIE](https://www.kaggle.com/code/predaddict/regression-prediction-intervals-with-mapie/edit) by Carl McBride Ellis (2022)

## Websites

1. [Main website with research from Prof. Vladimir (Volodya) Vovk](http://alrw.net/) 🔥🔥🔥🔥🔥
2. [Conformal Prediction - Prediction with guaranteed performance](https://cml.rhul.ac.uk/cp.html) Royal Holloway, United Kingdom
3. [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://people.eecs.berkeley.edu/~angelopoulos/blog/posts/gentle-intro/) by Anastasios N. Angelopoulos 🔥🔥🔥🔥🔥
4. [Reliable Predictive Inference](https://sites.google.com/view/cqr) by Yaniv Romano 🔥🔥🔥🔥🔥

## Twitter

1. [Title: What Can _Conformal Inference_ Offer to Statistics?](https://t.co/znZHcyyknV) by Lihua Lei, Stanford, 2022
2. [Conformalized survival analysis](https://twitter.com/lihua_lei_stat/status/1381418936739098630?s=20&t=PnfV8wnLV2bThXcKdFFbRg) by Lihua Lei, Stanford, 2021
3. [Conformal Risk Control](https://threadreaderapp.com/thread/1555616778578829312.html) by Anastasious Angelopolous, Berkeley, 2022
4. [Stable Conformal Prediction Sets](https://threadreaderapp.com/thread/1549413164835536897.html) by Eugene Ndiaye (Georgia Tech, 2022)
5. [Machine learning sucks at uncertainty quantification. But there is a solution that almost sounds too good to be true: conformal prediction](https://threadreaderapp.com/thread/1572526084981129217.html) by Cristoph Molnar (2022).
6. [How to correctly, yet efficiently model the uncertainty on predictions](https://threadreaderapp.com/thread/1584905067718647808.html) by Nico Wolf (2022)
7. [Top 10 Github libraries for Conformal Prediction](https://threadreaderapp.com/thread/1609112769017552897.html) by Valeriy Manokhin (2022)
8. [Robots That Ask For Help](https://threadreaderapp.com/thread/1677000811803443213.html) by Allen Z. Ren (2023) 🍾🍾🍾🍾🍾🔥🔥🔥🔥🔥

## TikTok

1. [Getting prediction intervals with conformal prediction](https://www.tiktok.com/@rajistics/video/7145960334270975275?is_from_webapp=v1&item_id=7145960334270975275) by Rajiv Shah (Hugging Face,2022)
2. [Why you want prediction intervals instead of point predictions](https://www.tiktok.com/@rajistics/video/7145295854927236398?is_from_webapp=v1&item_id=7145295854927236398) by Rajiv Shah (Hugging Face,2022)
3. [It’s important to make sure your model is well calibrated](https://www.tiktok.com/@rajistics/video/7164607746396933419?is_copy_url=1&is_from_webapp=v1&item_id=7164607746396933419) by Rajiv Shah (Hugging Face,2022)

## Conferences & Workshops

1. [11th Symposium on Conformal and Probabilistic Prediction with Applications](http://copa-conference.com/)
2. [IFDS Workshop on Conformal Prediction](https://ifds.info/ifds-madlab-workshop) 🔥🔥🔥🔥🔥
3. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2022](https://sites.google.com/berkeley.edu/dfuq-22/home) 🔥🔥🔥🔥🔥
4. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373)🔥🔥🔥🔥🔥
5. [10th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2021/) 🔥🔥🔥🔥🔥
6. [9th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2020/) 🔥🔥🔥🔥🔥
7. [8th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2019/) 🔥🔥🔥🔥🔥
8. [7th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2018/) 🔥🔥🔥🔥🔥
9. [6th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2017/) 🔥🔥🔥🔥🔥

## Python

1. [MAPIE - Model Agnostic Prediction Interval Estimator](https://github.com/scikit-learn-contrib/MAPIE) by Quantmetry team (2021) [Paper](https://arxiv.org/pdf/2207.12274.pdf) [slides](https://arxiv.org/pdf/2207.12274.pdf) 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

2. [Puncc (Predictive uncertainty calibration and conformalization)](https://github.com/deel-ai/puncc) [paper](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) 🔥🔥🔥🔥🔥

3. [‘Crêpes’ - Conformal regressors and predictive systems](https://github.com/henrikbostrom/crepes) by Henrik Boström (2021) [Paper](https://copa-conference.com/papers/COPA2022_paper_11.pdf) 🔥🔥🔥🔥🔥 [Presentation](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)

4. [Python implementation of binary and multi-class Venn-ABERS calibration](https://github.com/ip200/venn-abers) by Ivan Petej (2023) [Paper] 🔥🔥🔥🔥🔥

5. [EnbPI](https://github.com/hamrel-cxu/EnbPI) by Chen Xu (2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥 [Paper](https://proceedings.mlr.press/v139/xu21h.html)

6. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥🔥🔥🔥🔥

7. [TorchCP - A library for conformal prediction](https://github.com/ml-stat-Sustech/TorchCP) 🔥🔥🔥🔥🔥

8. [Venn-ABERS Predictor](https://github.com/ptocca/VennABERS) by Paolo Toccaceli (2019) [Paper](https://proceedings.neurips.cc/paper/2015/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html) 🔥🔥🔥🔥🔥

9. [Conformalized Quantile Regression](https://github.com/yromano/cqr) by Yaniv Romano (2019) 🔥🔥🔥🔥🔥

10. [Conformal Classification](https://github.com/aangelopoulos/conformal_classification) by Anastasios N. Angelopoulos (2021)

11. [Orange3 Conformal Prediction](https://github.com/biolab/orange3-conformal)

12. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019

13. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)

14. [TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch](https://github.com/TorchUQ/torchuq) (2022)

15. [Multi-class-probabilistic-classification using Venn-ABERS (Conformal) prediction](https://github.com/valeman/Multi-class-probabilistic-classification) by Valery Manokhin (Royal Holloway, 2022)

16. [Copula Conformal Multi Target Regression](https://github.com/M-Soundouss/CopulaConformalMTR) by Soundouss Messoudi (2021)

17. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)

18. [Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems](https://github.com/msesia/chr) by Mateo Sesia and Yaniv Romano (NeurIPS 2021). 🔥🔥🔥🔥🔥

19. [Conformalized density- and distance-based anomaly detection in time-series data (KNN-CAD)](https://github.com/numenta/NAB/tree/master/nab/detectors/knncad) by Evgeny Burnaev, Vladislav Ishimtsev (2016). Top #3 winning solution in Numenta competition 🔥🔥🔥🔥🔥

20. [Conformal time-series forecasting](https://github.com/kamilest/conformal-rnn) by Kamile ̇ Stankeviciute (Cambridge, NeurIPS 2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

21. [Valid Prediction Intervals](https://github.com/nmdwolf/ValidPredictionIntervals/tree/main/Code) by Nicolas Dewolf, Bernard DeBaets, Willem Waegeman (2022) 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

22. [Adaptive Conformal Predictions for Time Series](https://proceedings.mlr.press/v162/zaffran22a/zaffran22a.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥 [Video](https://www.youtube.com/watch?v=Yuxu9aUpVi0) [Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)

23. [Conformal learning from scratch](https://github.com/marharyta-aleksandrova/conformal-learning/blob/main/theory/conformal_learning_from_scratch.ipynb) by Marharyta Aleksandrova (2021)

24. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression) Vilde Jensen, Filippo Maria Bianchi and Stian Norman Anfinsen (2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

25. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://github.com/Shai128/rci) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

26. [PySloth - Python package for Probabilistic Prediction](https://github.com/PySloth/pysloth) by Valery Manokhin 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥

27. [Conformal Prediction in KNIME](https://hub.knime.com/tuwelofstrom/spaces/Predicting%20with%20Confidence/latest/~h8R_oa6iPRTssrYs/) by Tuwe Löfström and Redfield AB (2022)

28. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥🔥🔥🔥🔥

29. [SKTime](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.conformal.ConformalIntervals.html) by Franz Kiraly (2022)

30. [NeuralProphet](https://github.com/ourownstory/neural_prophet/blob/main/tutorials/feature-use/uncertainty_conformal_prediction.ipynb) (2022)🔥

31. [River](https://github.com/online-ml/river/blob/0506ad73e45403638f13d66c6b2d71920d307461/river/conf/jackknife.py#L9) 2022

32. [TorchUQ](https://github.com/TorchUQ/torchuq/blob/5335c5948385c7b9bde4baefc9399d79a7cb07ef/docs/tutorials/1_c_1_conformal/1_c_1_conformal.rst#L17) (2022)

33. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) [Paper](https://arxiv.org/pdf/2302.04019.pdf) by Amazon, (2022) TIME SERIES Events

34. Kaggle competition [“Prediction interval competition I: Birth weight.”](https://www.kaggle.com/competitions/prediction-interval-competition-i-birth-weight/discussion/459133) an opportunity to learn about and practice conformal prediction 🔥🔥🔥🔥🔥

## Books

1. [Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications](https://a.co/d/9guwCTm) by Valeriy Manokhin (2024) 🔥🔥🔥🔥🔥 [**Amazon USA 🇺🇸**](https://www.amazon.com/gp/aw/d/1805122762), [**Amazon UK 🇬🇧**](https://www.amazon.co.uk/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon India 🇮🇳**](https://www.amazon.in/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS), [**Amazon Germany 🇩🇪**](https://www.amazon.de/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/), [**Amazon France 🇫🇷**](https://www.amazon.fr/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Spain 🇪🇸**](https://www.amazon.es/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon Canada 🇨🇦**](https://www.amazon.ca/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Japan 🇯🇵**](https://www.amazon.co.jp/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/) 🔥🔥🔥🔥🔥🚀🚀🚀🚀🚀
2. [Algorithmic Learning in a Random World](https://link.springer.com/book/10.1007/978-3-031-06649-8) by Vladimir Vovk and Alex Gammerman, also Glenn Shafer (2022). Second edition. 🔥🔥🔥🔥🔥 great theory based book, very math heavy, no applications, no code
3. [Conformal Prediction for Reliable Machine Learning](https://www.elsevier.com/books/conformal-prediction-for-reliable-machine-learning/balasubramanian/978-0-12-398537-8) by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk (2014) OLD BOOK, largely out of date, no code

## Theses

1. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/361515440_Machine_Learning_for_Probabilistic_Prediction_PhD_thesis_VALERY_MANOKHIN), PhD Thesis, Valery Manokhin (Royal Holloway, UK, 2022) 🔥🔥🔥🔥🔥 (over 6K reads on Researchgate alone)
2. [Conformal and Venn Predictors for large, imbalanced and sparse chemoinformatics data](https://pure.royalholloway.ac.uk/portal/files/41316291/2021toccacelipphd.pdf), PhD Thesis, Paolo Toccaceli (Royal Holloway, UK, 2021)
3. [Competitive online algorithms for probabilistic prediction](https://pure.royalholloway.ac.uk/portal/files/36216771/Thesis_Raisa.pdf), PhD Thesis, Raisa Dzhamtyrova (Royal Holloway, UK, 2020)
4. [Conformal Prediction and Testing under On-line Compression Models](https://pure.royalholloway.ac.uk/portal/files/20318074/2014fedorovavphd.pdf), PhD Thesis, Valentina Fedorova (Royal Holloway, UK, 2014) 🔥🔥🔥🔥🔥
5. [Adaptive Online Learning](https://pure.royalholloway.ac.uk/portal/files/17462972/adamskiy_thesis.pdf), PhD Thesis, Dmitry Adamskiy (Royal Holloway, UK, 2013)
6. [On discovery and exploitation of temporal structure in data sets](https://pure.royalholloway.ac.uk/portal/files/24367944/Thesis.pdf), PhD Thesis, Tim Scarfe, (Royal Holloway, UK, 2015)
7. [Black-box Security Measuring Black-box Information Leakage via Machine Learning](https://pure.royalholloway.ac.uk/portal/files/33806285/thesis_final_after_amendments.pdf), PhD Thesis, Giovanni Cherubin (Royal Holloway, UK, 2019) 🔥🔥🔥🔥🔥
8. [Small and Large Scale Probabilistic Classifiers with Guarantees of Validity](https://pure.royalholloway.ac.uk/portal/files/30400650/2018PetejIPhd.pdf), PhD Thesis, Ivan Petej (Royal Holloway, UK, 2019) 🔥🔥🔥🔥🔥
9. [Confidence and Venn Machines and Their Applications to Proteomics](https://pure.royalholloway.ac.uk/portal/files/1402925/PhD_Thesis_Final_Dmitry_Devetyarov.pdf) by Devetyarov, Dmitry (Royal Holloway, UK, 2019)
10. [Conformal Anomaly Detection - detecting abnormal trajectories in surveillance applications](https://www.diva-portal.org/smash/get/diva2:690997/FULLTEXT02.pdf) by Rikard Laxhammar (University of Skoeve, Sweden, 2014) 🔥🔥🔥🔥🔥
11. [Inductive Confidence Machine for Pattern Recognition - is it the next step towards AI](https://github.com/eghamtech/AIOS/blob/master/notebooks/David_Surkov-RHUL-thesis-2004.pdf) by David Surkov (Royal Holloway, UK, 2004)
12. [Distribution Free Prediction Intervals for Multiple Functional Regression](http://d-scholarship.pitt.edu/39495/1/RMK%20Dissertation%20Final.pdf) by Ryan Kelly (University of Pittsburgh, 2020).
13. [Probabilistic Load Forecasting with Deep Conformalized Quantile Regression](https://munin.uit.no/bitstream/handle/10037/21914/thesis.pdf?sequence=2) by Vilde Jensen (Artcic University of Norway, 2021)
14. [Model-free methods for multiple testing and predictive inference](https://searchworks.stanford.edu/view/13876512), PhD Thesis, Zhimei Ren (Stanford, 2021) 🔥🔥🔥🔥🔥
15. [Comparison of Support Vector Machines and Deep Learning For QSAR with Conformal Prediction](https://www.diva-portal.org/smash/get/diva2:1682247/FULLTEXT01.pdf) by Deligianni Maria, MSc thesis, Universit of Uppsala (2022)
16. [Predictive Maintenance with Conformal and Probabilistic Prediction: A Commercial Case Study](https://www.jamesgammerman.com/files/Thesis.pdf) by James Gammerman (2022)
17. [Risk-Sensitive Decision-Making for Autonomous-Driving](https://www.diva-portal.org/smash/get/diva2:1698692/FULLTEXT01.pdf) by Hardy Hasan (University of Uppsala, 2022)
18. [Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction](https://arxiv.org/pdf/2210.14735.pdf), MSc thesis by Roel Hulsman, University of Oxford (2022)
19. [Coreset-based Protocols for Machine Learning Classification](https://pure.royalholloway.ac.uk/en/publications/coreset-based-protocols-for-machine-learning-classification), PhD thesis by Nery Riquelme Granada (Royal Holloway, University of London, 2022)
20. [Conformal survival predictions at a user-controlled time point](https://kth.diva-portal.org/smash/get/diva2:1231989/FULLTEXT01.pdf) by Jelle Van Miltenburg (KTH ROYAL INSTITUTE OF TECHNOLOGY, 2018)
21. [Reliable Machine Learning with ConformalPrediction: A Review with Contributions](https://www.researchgate.net/publication/366205428_Reliable_Machine_Learning_with_Conformal_Prediction_A_Review_with_Contributions) by Martim Sousa (2022) 🔥🔥🔥🔥🔥
22. [Nonconformity Measures and Ensemble Strategies - An Analysis of Conformal Predictor Efficiency and Validity](http://su.diva-portal.org/smash/get/diva2:1547120/FULLTEXT01.pdf), PhD thesis by Henrik Linusson (Stockholm University, 2021) 🔥🔥🔥🔥🔥
23. [Determine and explain confidence in predicting violations on inland ships in the Netherlands](https://repository.tudelft.nl/islandora/object/uuid:e0a9bb58-98ab-4c00-96ae-8701046a8012) by Bakker, Paul (TU Delft, 2020)
24. [Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0](https://www.diva-portal.org/smash/get/diva2:1765779/FULLTEXT01.pdf) by Shuzhou Liu, Mulahuko Mpova (Jönköping University, 2023).
25. [Benchmarking conformal prediction methods for time series regression](https://github.com/valeman/awesome-conformal-prediction/blob/main/BScThesis_DerckPrinzhorn.pdf) by Derck W.E. Prinzhorn (2023) [code](https://github.com/dweprinz/Benchmarking-conformal-prediction-methods-for-time-series-regression) 🔥🔥🔥🔥🔥
26. [Conformal Prediction Methods in Finance](https://impa.br/wp-content/uploads/2022/11/Projeto_Final_Joao-Vitor-Romano.pdf) by Finance João Vitor Romano (Instituto de Matemática Pura e Aplicada, Brazil, 2022) 🔥🔥🔥🔥🔥
27. [An Active Learning Approach for Reject Inference in Credit Scoring using Conformal Prediction Intervals on Real and Semi-Artificial Data](https://github.com/MaximilianSuliga/Conformal-Active-Learning-for-Reject-Inference) by Maximilian Suliga (Humboldt University of Berlin, 2023)
28. [Confidence Predictions in Pharmaceutical Sciences](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1787351&dswid=-7234) by Staffan Arvidsson McShane (Uppsala University, Sweden, 2023) 🔥🔥🔥🔥🔥
29. [Conform With the Wind Processing short-term ensemble forecasts with conformal based methods for probabilistic wind-speed forecasting](https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9130213&fileOId=9130294), MSc thesis by Simon Althoff (Lund University, 2023)
30. [Trustworthy explanations: Improved decision support through well-calibrated uncertainty quantification](https://hj.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=4&af=%5B%5D&searchType=SIMPLE&sortOrder2=title_sort_asc&query=Helena+L%C3%B6fstr%C3%B6m&language=en&pid=diva2%3A1810440&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-3088), PhD thesis by Helena Löfström (Jönköping Universitu, 2023) 🔥🔥🔥🔥🔥
31. [Data Augmentation and Conformal Prediction](https://dspace.mit.edu/handle/1721.1/151275), MSc thesis by Helen Lu (MIT, 2023)
32. [Conformal prediction and copula based methods for profile monitioring](https://www.politesi.polimi.it/handle/10589/214268), MSc Thesis by Niccolo’ Donadini (Politecnico di Milano, 2023)

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**Author:** ![Aina](https://onehack.st/user_avatar/onehack.st/aina/32/167469_2.png) [@Aina](https://onehack.st/u/Aina)\
**Post date:** [January 12, 2024, 8:55pm UTC](https://onehack.st/t/awesome-conformal-prediction-massive-resources-collection/272000/2 "2024-01-12T20:55:06Z")

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> **Continue Reading & Following...**
>
> ## Tutorials
> 
> 1. [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://arxiv.org/pdf/2107.07511.pdf) by Anastasios N. Angelopoulos and Stephen Bates (2021) [Video](https://www.youtube.com/watch?v=nql000Lu_iE&t=1786s) [Code](https://github.com/aangelopoulos/conformal-prediction) 🔥
> 2. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (2020) 🔥
> 3. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) 🔥
> 4. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020) 🔥
> 5. [Predicting with Confidence - Henrik Boström](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Boström (2016) 🔥
> 6. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sönströd, Tuve Löfström, and Henrik Boström (2021) 🔥
> 7. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (2020) 🔥
> 8. [A Tutorial on Conformal Prediction](https://jmlr.org/papers/v9/shafer08a.html) by Glenn Shafer and Vladimir Vovk (2008) 🔥
> 9. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana1 and Simone Vantini (Politecnico di Milano, Italy, 2021) 🔥
> 10. [Conformal Prediction in Spark](https://docs.google.com/presentation/d/1eD1vUJVR3nejyJZsOMfxL-NxrLy7K2UUMSAEMHYLfek/edit#slide=id.g2276269343_0_129) by Marco Capuccini (Uppsala University, 2017)
> 11. [Tutorial on Venn-ABERS prediction](https://cml.rhul.ac.uk/people/ptocca/HomePage/Toccaceli_CP___Venn_Tutorial.pdf) by Paolo Toccaceli (Royal Holloway, UK, 2019) 🔥
> 12. [An Introduction to Conformal Prediction](https://cml.rhul.ac.uk/copa2017/presentations/CP_Tutorial_2017.pdf) by Henrik Linusson (2017)
> 13. [Introduction to Conformal Prediction](https://www.iith.ac.in/~vineethnb/indoukworkshop2015/assets/files/IntroToCP.pdf) by Vineeth N Balasubramanian (Indian Institue of Technology, Hyderabad, 2015)
> 14. [Conformal prediction A Tiny Tutorial on Predicting with Confidence](https://people.dsv.su.se/~henke/DSWS/johansson.pdf) by Henrik Linusson and Ulf Johansson (2014)
> 15. [Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://www.youtube.com/watch?v=usaHyuu2TzY) by Anastasios Angelopoulos (Berkeley, 2022)
> 16. [A Tutorial on Conformal Prediction Part 2: Conditional Coverage and Diagnostics](https://www.youtube.com/watch?v=TRx4a2u-j7M&t=529s) by Anastasios Angelopoulos and Stephen Bates (2022) 🔥
> 17. [Beyond Conformal Prediction: Tutorial on Conformal Part 3](https://www.youtube.com/watch?v=37HKrmA5gJE) by Anastasios Angelopoulos and Stephen Bates (2022) 🔥
> 18. [Getting predictions intervals with conformal inference](http://projects.rajivshah.com/blog/2022/09/24/conformal_predictions) by Rajiv Shah (2022) [YouTube](https://www.youtube.com/watch?v=ZUK4zR0IeLU) [Code](https://colab.research.google.com/drive/1bA_TrrmRpgJ0jasWBZCxkXSLePi8uWBx) 🔥
> 19. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, USC, 2022) 🔥
> 20. [Regression prediction intervals with MAPIE on Kaggle](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie) by Carl McBride Ellis, PhD
> 21. [Distribution-free inference tutorial At the IFDS 2021 Summer School](https://rinafb.github.io/talks/) [Video 1](https://vimeo.com/581870072) [Video 2](https://vimeo.com/581870078)
> 22. [Conformal Inference Tutorial](https://bkompa.github.io/2020/09/19/Conformal-Inference-Tutorial.html) by Ben Kompa (2020)
> 23. [Prediction intervals for any machine learning model - How to construct prediction intervals with the Jackknife+ using the MAPIE package](https://www.valencekjell.com/posts/2022-09-14-prediction-intervals/) by Kjell Jorner (ETH, 2022)
> 24. [Uncertainty Quantification (1): Enter Conformal Predictors](https://www.youtube.com/watch?v=xZbuFKWV5NA&t=25s) by Mahdi Torabi Rad (2023) 🔥
> 25. [Uncertainty Quantification (2): Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) 🔥
> 26. [Uncertainty Quantification (3): From Full to Split Conformal Methods](https://www.youtube.com/watch?v=YigGJfsCjDk) by Mahdi Torabi Rad (2023) 🔥
> 27. [Conformal Prediction and Venn Predictors A Tutorial on Predicting with Confidence](https://icdatascience.org/wp-content/uploads/2019/07/ICDATA_tutorial_2019_Johansson_U.pdf) by Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström, Alex Gammerman (2019) 🔥
> 28. [Tutorial on Conformal Prediction Claire Boyer Assistant Professor, LPSM, Paris Sorbonne Université ; Margaux Zaffran PhD Candidate, EDF, Inria, CMAP, École Polytechnique](https://claireboyer.github.io/tutorial-conformal-prediction/) 🔥
> 29. [Conformal Prediction: How to quantify uncertainty of machine learning models? ECAS-ENBIS course– ENBIS 2023 Annual conference](https://mzaffran.github.io/assets/files/Talks/Tuto_CP_ENBIS_ECAS.pdf) by Margaux Zaffran 🔥 (2023)
> 30. [Conformal Prediction in Genomics](https://github.com/BiolApps/ConformalPrediction/tree/main) by BiolApps (2023)
> 31. [Conformal Prediction: A Visual Introduction](https://conformal-prediction.streamlit.app/) in [VISxAI](https://visxai.io/) by Mihir Agarwal, Lalit Chandra Routhu, Zeel B Patel and Nipun Batra (IIT Gandhinagar, IIT Patna, 2023)
> 32. [A Conformal Prediction tutorial, an introductive review of the basics](https://conformalpredictionintro.github.io/) by Margaux Zaffran (2023)
> 33. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) 🔥
> 
> ## Courses
> 
> 1. [Uncertain: Modern topics in uncertainty estimation](https://uncertaintyclass.com/) [YouTube](https://www.youtube.com/watch?app=desktop&v=M3tkM4dcIPA) [Course notes](https://www.cis.upenn.edu/~aaroth/uncertainty-notes.pdf) by Aaron Roth (University of Pennsylvania, 2022) 🔥
> 2. [Topics in Modern Statistical Learning](https://github.com/dobriban/Topics-In-Modern-Statistical-Learning) by Edgar Dobriban (Wharton School, University of Pennsylvania, 2022) 🔥
> 3. [Theory of Statistics](https://candes.su.domains/teaching/stats300c/) Stanford Statistics course by Prof Emmanuel Candes (2022) 🔥
> 4. [36-708 Statistical Methods for Machine Learning](https://www.stat.cmu.edu/~larry/=sml/) Carnegie-Mellon course by Prof Larry Wasserman (2022)
> 5. [Course on Conformal Prediction](https://mindfulmodeler.substack.com/p/e-mail-course-on-conformal-prediction) by Christoph Molnar (2022)
> 6. [Conformal Prediction - Advanced Topics in Statistical Learning, Spring 2023](https://www.stat.berkeley.edu/~ryantibs/statlearn-s23/lectures/conformal.pdf) by Ryan Tibshirani (2023) 🔥
> 7. [Conformal Methods for Efficient and Reliable Deep Learning](https://dspace.mit.edu/handle/1721.1/152788) by Adam, Fisch (MIT, 2023) 🔥
> 8. [Conformal Prediction 101 - in Portuguese 🇵🇹](https://github.com/gusbruschi13/Conformal-Prediction/tree/main/cp-101) by Gustavo Bruschi (2023) 🔥
> 
> ## Videos
> 
> 1. [Treatment of Uncertainty in the Foundations of Probability](https://www.youtube.com/watch?v=B7E-QJ9fm4w&t=2297s) by Vladimir Vovk (Royal Holloway, UK, 2017)
> 2. [Large-Scale Probabilistic Prediction With and Without Validity Guarantees](https://www.youtube.com/watch?v=ksrUJdb2tA8) by Vladimir Vovk (Royal Holloway, UK, NeurIPS 2015) 🔥
> 3. [Conformal testing in a binary model situation](https://www.youtube.com/watch?v=RTcT4YXRdMg) by Vladimir Vovk (Royal Holloway, UK, 2021)
> 4. [Protected probabilistic classification](https://www.youtube.com/watch?v=MpP-3suUoLY) by Vladimir Vovk (Royal Holloway, UK, 2021)
> 5. [Retrain or not retrain: conformal test martingales for change-point detection](https://www.youtube.com/watch?v=4Ra5KnDEfkw&t=1s) by Vladimir Vovk (Royal Holloway, UK, 2021) 🔥
> 6. [A Tutorial on Conformal Prediction](https://www.youtube.com/watch?v=nql000Lu_iE&t=1786s) by Anastasios Angelopoulos and Stephen Bates (Berkeley, ICML 2021) 🔥
> 7. [Steps Toward Trustworthy Machine Learning](https://www.youtube.com/watch?v=2iNRSgS7-L4) by Tom Dietterich (2021)
> 8. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (Royal Holloway, UK, 2020) 🔥
> 9. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) 🔥
> 10. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020)
> 11. [Predicting with Confidence - Henrik Boström](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Boström (2016)
> 12. [How to increase certainty in predictive modeling](https://www.youtube.com/watch?v=fQQP84yxCRs&t=1277s) by Emmanuel Candes (Stanford, 2021) 🔥
> 13. [Recent Progress in Predictive Inference](https://www.youtube.com/watch?v=tY73G_UvkAE&t=833s) by Emmanuel Candes (Stanford, 2020) 🔥
> 14. [Some recent progress in predictive inference" (Stanford) @ MAD+](https://www.youtube.com/watch?v=djgxwpJQyAA) by Emmanuel Candes (Stanford, 2020)
> 15. [Conformal Prediction in 2020](https://www.youtube.com/watch?v=61tpigfLHso&t=1507s) by Emmanuel Candes (Stanford, 2020) 🔥
> 16. [Assumption-free prediction intervals for black-box regression algorithms](https://www.youtube.com/watch?v=GMnCO7_HIOY&t=3943s) by Aaditya Ramdas (Carnegie Mellon, 2020) 🔥
> 17. [Maria Navarro: Quantifying uncertainty in Machine Learning predictions | PyData London 2019](https://www.youtube.com/watch?v=r6bhm_A-YcQ&t=12s) by Maria Navarro (2019)
> 18. [Conformal Prediction: Enhanced Method for Understanding the Prediction Quality](https://www.youtube.com/watch?v=_ZVuEWEfwuw&t=948s) by Artem Ryasik and Greg Landrum
> 19. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, and Henrik Boström (2021)
> 20. [Mondrian conformal predictive distributions](https://www.youtube.com/watch?v=dHNZxw8WQrs) by Henrik Boström, Ulf Johansson, and Tuwe Löfström (2021) 🔥
> 21. [Calibrating Multi-Class Models](https://www.youtube.com/watch?v=uCnFgTjUYto) by Ulf Johansson, Tuwe Löfström, and Henrik Boström (2021)
> 22. [Conformal testing in a binary model situation](https://www.youtube.com/watch?v=RTcT4YXRdMg) by Vladimir Vovk (Royal Holloway, UK, 2021)
> 23. [Conformal prediction in Orange](https://www.youtube.com/watch?v=qI1jOEour1g&t=14s) by Tomaž Hočevar and Blaž Zupan (2021)
> 24. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=ITJAR3fcNuI) by Anastasios Angelopoulos (Stanford, 2021) 🔥
> 25. [Conformal Prediction and Distribution-Free Calibration](https://synapse.math.univ-toulouse.fr/s/KDcWmmU9j9zk0rm) by Aaditya Ramdas (Carnegie Mellon, 2021) 🔥
> 26. [Reliable Diagnostics by Conformal Predictors](https://www.youtube.com/watch?v=zW3R-vbLw58) by Alexander Gammerman (Royal Holloway, UK, 2015)
> 27. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=ITJAR3fcNuI) by Anastasios Angelopoulos (Stanford, 2021) 🔥
> 28. [Conformal Inference of Counterfactuals and Time-to-event Outcomes](https://www.youtube.com/watch?v=nfD3mrSefbI) by Lihua Lei (Stanford, 2021)
> 29. [Algo Hour – Conformal Inference of Counterfactuals and Individual Treatment Effect](https://www.youtube.com/watch?v=COW2QNBmEMw) by Lihua Lei (Stanford, 2021)
> 30. [Conformal Inference of Counterfactuals and Individual Treatment effects(Stanford)](https://www.youtube.com/watch?v=8tM4BhONHms) by Lihua Lei (Stanford, 2021)
> 31. [Approximation to object conditional validity with inductive conformal predictors](https://www.youtube.com/watch?v=pUf7z2vxdi8) by Anthony Bellotti (University of Nottingham Ningbo, China, 2021)
> 32. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=40s) by Ulf Johansson (Jönköping University, Sweden, 2021) 🔥
> 33. [Transformer-based conformal predictors for paraphrase detection](https://www.youtube.com/watch?v=HYP1ypxywWo) by Patrizio Giavannotti and Prof. Alexander Gammerman (Royal Holloway, UK, 2021)
> 34. [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://www.youtube.com/watch?v=jkFs6pLZXBQ) by Lihua Lei (Stanford, 2020)
> 35. [Model-Free Predictive Inference](https://www.youtube.com/watch?v=8GkhRuWcd0w) by Larry Wasserman (Carnegie Mellon, 2020) 🔥
> 36. [Shapley-value based inductive conformal prediction](https://www.youtube.com/watch?v=6XUc3HFa_5Q&t=1094s) by William Lopez Jaramillo (2021)
> 37. [Conformal Training: Learning Optimal Conformal Classifiers | DeepMind](https://www.youtube.com/watch?v=XMa1glDpVtQ&t=318s) by David Stutz (2021) 🔥
> 38. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=z8WDmD5D-I0) by Anastasios Angelopoulos (2021) 🔥
> 39. [Assumption-Free, High-Dimensional Inference](https://www.youtube.com/watch?v=UOMvUaYfpH4) by Larry Wasserman (2016)
> 40. [Neural Predictive Monitoring under Partial Observability](https://www.youtube.com/watch?v=JhJXUDPoKCc) by Francesca Cairolli (2021)
> 41. [Conformalized Kernel Ridge Regression and Its Efficiency](https://www.youtube.com/watch?v=OLeu9TXE5n4) by Evgeny Burnaev (Skolkovo, Russia, 2015)
> 42. [Fast conformal classification using influence functions](https://www.youtube.com/watch?v=LRwm976poDE) by Giovanni Cherubin (Alan Turing Institute, UK, 2021)
> 43. [Valid inferential models and conformal prediction](https://www.youtube.com/watch?v=egrLw0CmXTs) by Ryan Martin (North Carolina State University, USA, 2021)
> 44. [Mondrian conformal predictive distributions](https://www.youtube.com/watch?v=dHNZxw8WQrs&t=1415s) by Henrik Boström, Ulf Johansson and Tuwe Löfström (KTH Royal Institute of Technology, Sweden, 2021) 🔥
> 45. [Evaluation of updating strategies for conformal predictive systems in the presence of extreme events](https://www.youtube.com/watch?v=Xgs0JqDw8lA) by Hugo Werner, Lars Carlsson, Ernst Ahlberg and and Henrik Boström (KTH Royal Institute of Technology, Sweden, 2021)
> 46. [Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data](https://www.youtube.com/watch?v=Wcm9Uw0YL8A) by Victor Chernozhukov (MIT, USA, 2019) 🔥
> 47. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (Jönköping University, Sweden, 2020) 🔥
> 48. [Class-wise confidence for debt prediction in real estate management](https://www.youtube.com/watch?v=ZVhA8LGXWpc) by Soundouss Messoudi (2021)
> 49. [How Nonconformity Functions and Difficulty of Datasets Impact the Efficiency of Conformal Classifiers](https://www.youtube.com/watch?v=lLtZkVwxMNw) by Marharyta Aleksandrova (2021)
> 50. [Nested conformal prediction and quantile out-of-bag ensemble methods](https://www.youtube.com/watch?v=NlUlelNWVMQ) by Chirag Gupta (Carnegie Mellon, 2020) 🔥
> 51. [Panel with Michael I. Jordan, Vladimir Vovk, and Larry Wasserman, moderated by Aaditya Ramdas](https://slideslive.com/38964850/panel-with-michael-i-jordan-vladimir-vovk-and-larry-wasserman-moderated-by-aaditya-ramdas?ref=account-folder-87373-folders) by Vladimir Vovk, Larry Wasserman, Michael I. Jordan, Aaditya Ramdas, ICML 2021 🔥🔥🔥🔥🔥 🔥🔥🔥🔥🔥
> 52. [Black-box uncertainty - Anastasios Angelopoulos](https://www.youtube.com/watch?v=jW-mbsVgcIc) by Anastasios Angelopoulos (Berkeley, USA, 2021) 🔥
> 53. [P.C. Mahalanobis Memorial Lectures 2020-21](https://www.isibang.ac.in/~statmath/pcm2020/) by Vladimir Vovk (Royal Holloway, UK, 2021)
> 54. [Rahul Vishwakarma: New Perspective on Machine Learning Predictions Under Uncertainty | SNIA Storage Developer Conference, Santa Clara 2019](https://www.youtube.com/watch?v=T-hG1JyAk4E) by Rahul Vishwakarma (2019)
> 55. [Fast conformal classification using influence functions](https://www.youtube.com/watch?v=LRwm976poDE) by Umang Bhatt, Adrian Weller and Giovanni Cherubin (Cambridge / Alan Turinig Institute, 2021).
> 56. [Adaptive Conformal Predictions for Time Series | ISDFS](https://www.youtube.com/watch?v=Yuxu9aUpVi0) by Margaux Zaffran (2022) TIME SERIES 🚀🔥[Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
> 57. [Recent progress in predictive inference](https://www.youtube.com/watch?v=FQxpdMME0qU) by Emmanuel Candes, Stanford University (2022)
> 58. [Conformalized Survival Analysis with Adaptive Cutoffs](https://youtu.be/bvZ0SZlnkv8) by Rina Foygel Barber, Zhimei Ren, Yu Gui and Rohan Hore, University of Chicago (2022)
> 59. [Calibrating probabilistic hierarchical forecasts with conformal predictions](https://www.youtube.com/watch?v=VNhgk8Q1VFg) by Daan Ferdinandusse (University of Amsterdam, 2022)
> 60. [Michael I. Jordan on Conformal Prediction](https://www.youtube.com/watch?v=kSGP4F_ZcBY) by Michael I. Jordan (Berkeley, 2022)
> 61. [Distribution-free Prediction: Exchangeability and Beyond](https://www.youtube.com/watch?v=HVZDLhW8Kxg) by Rina Foygel Barber (University of Chicago, 2022)
> 62. [Purdue Statistics Theme Seminar, Conformal Prediction in 2022](https://www.youtube.com/watch?v=pjhp1moPvO4) by Emmanuel Candes (Stanford, 2022)
> 63. [WILL MY ROBOT ACHIEVE MY GOALS? PREDICTING THE PROBABILITY THAT AN MDP POLICY REACHES A USER-SPECIFIED BEHAVIOR TARGET](https://arxiv.org/pdf/2211.16462.pdf) by Alexander Guyer and Thomas G. Dietterich (University of Oregon, 2022)
> 64. [Robust and Equitable Uncertainty Estimation](https://www.youtube.com/watch?v=Iznj13jP9ag) by Aaron Roth(2022)
> 65. [Conformal prediction under feedback covariate shift for biomolecular design](https://www.youtube.com/watch?v=AOyDjBSQjhk) by Clara Wong-Fannjiang (Berkeley, 2022)
> 66. [Conformal prediction in 2022](https://slideslive.com/38996063/conformal-prediction-in-2022?ref=speaker-43789) invited talk by Emmanuel Candes at NeurIPS2022 🔥
> 67. [Broadening the Scope of Conformal Inference](https://slideslive.com/38988210/broadening-the-scope-of-conformal-inference?ref=search-presentations-Conformal+prediction) by Michael I. Jordan (University of Berkeley, 2022) 🔥
> 68. [Paper Reading Group - Fortuna, a Library for Uncertainty Quantification](https://www.youtube.com/watch?app=desktop&v=S_QgP3jBCqw)
> 69. [CLIMB Evergreen talk with Emmanuel Candès: Conformal Inference when Data is not Exchangeable](https://www.youtube.com/watch?v=D60vtpnkhTU) TIME SERIES 🚀🔥
> 70. [Algo Hour – Conformal Inference of Counterfactuals and Individual Treatment Effects | Lihua Lei](https://www.youtube.com/watch?v=COW2QNBmEMw&t=2s)
> 71. ['MoroccoAI webinar - Dr. Soundouss Messoudi - ‘Confidence learning using conformal prediction’](https://www.youtube.com/watch?v=SypbjiBrt3Q)
> 72. [Emmanuel Candes - A Taste of Conformal Prediction](https://www.carmin.tv/en/video/a-taste-of-conformal-prediction) by Emmanuel Candes (2023)
> 73. [Foundations of Conformal Prediction - Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) 🔥
> 74. [Max Mergenthaler and Fede Garza - Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023) TIME SERIES 🚀🔥
> 75. [Uncertainty Quantification with the Fortuna library by Gianluca Detommaso (AWS)](https://www.youtube.com/watch?v=FEtD6lnsI58) by Gianluca Detommaso (Amazon, 2023)
> 76. [Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023)
> 77. [NISS/Merck Meetup on Conformal Inference: Advancing the Boundaries of Machine Learning 4.19.2023](https://www.youtube.com/watch?v=SRbqUae9-_o&t=5631s) (2023) 🔥
> 78. [Max Kuhn - The Post-Modeling Model to Fix the Model](https://www.youtube.com/watch?v=3omi4lm1da0) by Max Kuhn (2023)🔥
> 79. [ISDFS Talk: Robots that ask for help: Conformal Prediction for LLM Planners](https://www.youtube.com/watch?v=WnQopZKJsTw) by Anirudha Majumdar (Princeton/DeepMind) 🔥(2023)
> 80. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://www.youtube.com/watch?v=xvXrPdPb3Ko) by Allen Z Ren (2023) 🔥
> 81. [Conformal Prediction & Complex Data Analytics] ([https://www.youtube.com/watch?v=PbHsmPupFak](https://www.youtube.com/watch?v=PbHsmPupFak)) by Matteo Fontana (2023) 🔥
> 82. [Three Easy Steps to Understand Conformal Prediction (CP), Conformity Score, Python Implementation](https://www.youtube.com/watch?v=oqK6rM8fbkk) by (Dr. Data Science 2023) 🔥
> 83. [Rising Stars #8: Clara Wong-Fannjiang (Genentech) - Prediction-Powered Inference](https://www.youtube.com/watch?v=TlFpVpFx7JY)
> 84. [Rising Stars #10 - Special Series on Conformal Prediction: Isaac Gibbs (Stanford University) Conformal Inference with Conditional Guarantees](https://www.youtube.com/watch?v=rvYnR0FGxM4)
> 85. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://www.youtube.com/watch?v=QhvLaZ1aS6A) by Andreas Auer TIME SERIES 🚀🔥
> 86. [Introducing Sequential Predictive Conformal Inference (SPCI)](https://www.youtube.com/watch?v=NQHrTMZ2-XA&t=346) by Chen Xu (2023) TIME SERIES 🚀🔥
> 87. [Conformal Prediction Intervals: Empowering Executives for Informed Decision](https://www.youtube.com/watch?v=3StianQJZTk&t=26s) by Matthew Kolakowski (2023) 🔥
> 88. [Conformal Inference with Tidymodels - posit::conf(2023)](https://www.youtube.com/watch?v=vJ4BYJSg734) by Max Kuhn (2023)
> 89. [ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles](https://www.youtube.com/watch?v=8PwDHAITuwU) by Sangdon Park (2023)
> 90. [Leveraging conformal prediction for calibrated probabilistic time series forecast](https://www.youtube.com/watch?v=--WcrDRtrYk) by Inge van den Ende (Dexter Energy, 2023) TIME SERIES 🚀🔥
> 91. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://www.youtube.com/watch?v=Yq9cvqEk2K8&t=8s) by Kexin Huang (Stanford, 2023) 🔥
> 92. [Selection by Prediction with Conformal p-values](https://www.youtube.com/watch?v=VHcCmkChwTk&t=5s) by Ying Jin (Stanford, 2023)
> 93. [Trustworthy Retrieval Augmented Chatbots Utilizing Conformal Predictors](https://www.youtube.com/watch?v=JnWXebWUEg4) by Shuo Li (UPenn, 2023).
> 
> ## Papers
> 
> 1. [Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination](https://pubs.acs.org/doi/10.1021/ci5001168) by Ulf Norinder, Lars Carlsson, Scott Boyer, and Martin Eklund (2014)
> 2. [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://arxiv.org/pdf/2009.14193.pdf) by Anastasios N. Angelopoulos, Stephen Bates, Jitendra Malik, & Michael I. Jordan (Berkeley, 2021) 🔥
> 3. [Conformal Prediction Under Covariate Shift](https://arxiv.org/abs/1904.06019) by Ryan Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas (Carnegie Mellon, Stanford, Chicago, 2019) 🔥
> 4. [Regression Conformal Prediction with Nearest Neighbours](https://arxiv.org/pdf/1401.3880.pdf) by Harris Papadopoulos, Vladimir Vovk and Alex Gammerman (Royal Holloway, UK, 2014) 🔥
> 5. [Nested conformal prediction and quantile out-of-bag ensemble methods](https://arxiv.org/pdf/1910.10562.pdf) by Chirag Gupta, Arun Kuchibhotla and Aaditya Ramdas (Carnegie Mellon, 2021) 🔥
> 6. [Cross-conformal predictive distributions](http://proceedings.mlr.press/v91/vovk18a.html) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin and Alexander Gammerman (Royal Holloway, UK, 2018) 🔥
> 7. [Criteria of Efficiency for Conformal Prediction](https://arxiv.org/pdf/1603.04416.pdf) by Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, and Alex Gammerman ((Royal Holloway, UK, 2016)
> 8. [Conformal Prediction for Simulation Models](https://benjaminleroy.github.io/documents/icml2021/conformal_prediction_for_simulation_models.pdf) by Benjamin LeRoy and Chad Schafer (Carnegie Mellon, 2021)
> 9. [Distribution-free, risk-controlling prediction sets](https://arxiv.org/pdf/2101.02703) Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik and Michael I Jordan (Berkeley, 2021) 🔥🔥🔥🔥🔥
> 10. [Conditional calibration for false discovery rate control under dependence](https://arxiv.org/abs/2007.10438) by William Fithian and Lihua Lei (Stanford, 2021)
> 11. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana and S. Vantini (2021) 🔥
> 12. [Regression conformal prediction with random forests](https://link.springer.com/content/pdf/10.1007%2Fs10994-014-5453-0.pdf) by Ulf Johansson, Henrik Boström, Tuve Löfström and Henrik Linusson (2014)
> 13. [A conformal prediction approach to explore functional data](https://arxiv.org/pdf/1302.6452.pdf) by Jing Lei, Alessandro Rinaldo, Larry Wasserman (Carnegie Mellon, 2013)
> 14. [An electronic nose-based assistive diagnostic prototype for lung cancer detection with conformal prediction](https://pure.royalholloway.ac.uk/portal/files/37171406/Measurement_Manuscript_unmarked_R4.pdf) by Xianghao Zhana,c, Zhan Wanga, Meng Yangb, Zhiyuan Luod, You Wanga, Guang Li (2020)
> 15. [Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework](https://pubmed.ncbi.nlm.nih.gov/28335598/) by Martin Lindh, A. Karlén, Ulf Norinder (2017)
> 16. [The application of conformal prediction to the drug discovery process](https://link.springer.com/article/10.1007%2Fs10472-013-9378-2) by Martin Eklund, Ulf Norinder, Scott Boyer & Lars Carlsson (2014) 🔥
> 17. [Distributional conformal prediction](https://arxiv.org/pdf/1909.07889.pdf) by Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu (2021) 🔥
> 18. [Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction](https://link.springer.com/content/pdf/10.1007%2F978-3-662-44722-2_29.pdf) by James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, and Alexander Gammerman (2009)
> 19. [Conformal prediction interval estimation and applications to day-ahead and intraday power markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath, Florian Ziel (2019) 🔥
> 20. [The application of conformal prediction to the drug discovery process](https://link.springer.com/article/10.1007%2Fs10472-013-9378-2) by Martin Eklund, Ulf Norinder, Scott Boyer & Lars Carlsson (2013)
> 21. [Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction](https://link.springer.com/chapter/10.1007%2F978-3-662-44722-2_29) by James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, Alexander Gammerman (Royal Holloway, UK, 2014)
> 22. [Conformal Prediction: a Unified Review of Theory and New Challenges](https://www.e-publications.org/ims/submission/BEJ/user/submissionFile/46245?confirm=193b4e5b) by Gianluca Zeni, Matteo Fontana1 and Simone Vantini (Politecnico di Milano, Italy, 2021)
> 23. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
> 24. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (Yale, 2020)
> 25. [Predicting skin sensitizers with confidence - Using conformal prediction to determine applicability domain of GARD](https://pubmed.ncbi.nlm.nih.gov/29374571/) by Andy Forreryd, Ulf Norinder, Tim Lindberg, Malin Lindstedt (2018)
> 26. [Binary classification of imbalanced datasets using conformal prediction](https://pubmed.ncbi.nlm.nih.gov/28135672/) by Ulf Norinder, Scott Boyer (2017)
> 27. [Discretized conformal prediction for efficient distribution-free inference](https://arxiv.org/pdf/1709.06233.pdf) by Wenyu Chen, Kelli-Jean Chun, and Rina Foygel Barber (2017)
> 28. [Validity, consonant plausibility measures, and conformal prediction](https://www.sciencedirect.com/science/article/abs/pii/S0888613X21001195?via%3Dihub) by Leonardo Cella. and Ryan Martin (2021)
> 29. [Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays](https://pubmed.ncbi.nlm.nih.gov/27152554/) by Ulf Norinder, Scott Boyer (2016)
> 30. [Conformal prediction to define applicability domain – A case study on predicting ER and AR binding](https://www.tandfonline.com/doi/full/10.1080/1062936X.2016.1172665) by U. Norinder, A. Rybacka, P.Andersson (2016)
> 31. [Conformal prediction of biological activity of chemical compounds](https://link.springer.com/article/10.1007%2Fs10472-017-9556-8) by Paolo Toccaceli, Ilia Nouretdinov, Alex Gammerman (Royal Holloway, UK, 2017) 🔥
> 32. [Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination](https://pubmed.ncbi.nlm.nih.gov/25559551/) by Ulf Norinder, Lars Carlsson, Scott Boyer, Martin Eklund (2015)
> 33. [Aggregated Conformal Prediction](https://link.springer.com/chapter/10.1007%2F978-3-662-44722-2_25) by Lars CarlssonMartin EklundUlf Norinder (2014)
> 34. [Interpretation of Conformal Prediction Classification Models](https://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_27) by Ernst Ahlberg, Ola Spjuth, Catrin Hasselgren, Lars Carlsson (2015)
> 35. [Cross-Conformal Prediction with Ridge Regression](https://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_21) by Harris Papadopoulos (2015)
> 36. [Sparse conformal prediction for dissimilarity data](https://link.springer.com/article/10.1007%2Fs10472-014-9402-1) by Frank-Michael Schleif, Xibin Zhu and Barbara Hammer (2015)
> 37. [Effective utilization of data in inductive conformal prediction using ensembles of neural networks](https://ieeexplore.ieee.org/document/6706817) by Tuve Löfström, Ulf Johansson and Henrik Boström (2013)
> 38. [Beyond the Basic Conformal Prediction Framework](https://www.sciencedirect.com/science/article/pii/B978012398537800002X?via%3Dihub) by Vladimir Vovk (2014)
> 39. [An electronic nose-based assistive diagnostic prototype for lung cancer detection with conformal prediction](https://pure.royalholloway.ac.uk/portal/files/37171406/Measurement_Manuscript_unmarked_R4.pdf) by Xianghao Zhan, Zhan Wang, Meng Yang, Zhiyuan Luo, You Wang, Guang Li (Stanford, Royal Holloway, China University of Mining and Technology, 2020)
> 40. [Predicting with confidence: Using conformal prediction in drug discovery](https://jpharmsci.org/article/S0022-3549(20)30589-X/fulltext) by Jonathan Alvarsson, Staffan Arvidsson McShane, Ulf Norinder, Ola Spjuth (2021) 🔥
> 41. [Inductive conformal prediction for silent speech recognition](https://pubmed.ncbi.nlm.nih.gov/32120355/) by Ming Zhang, You Wang, Zhang Wei, Meng Yang, Zhiyuan Luo, Guang Li (2020)
> 42. [Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0325-4) by Nicolas Bosc, Francis Atkinson, Eloy Felix, Anna Gaulton, Anne Hersey and Andrew R. Leach (2019)
> 43. [Deep Conformal Prediction for Robust Models](https://link.springer.com/chapter/10.1007%2F978-3-030-50146-4_39) by Soundouss Messoudi, Sylvain Rousseau and Sébastien Destercke (2020)
> 44. [Strong validity, consonance, and conformal prediction](https://arxiv.org/abs/2001.09225) by Leonardo Cella and Ryan Martin (2020)
> 45. [Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules](https://pubs.acs.org/doi/10.1021/acs.chemrestox.0c00253) by Anke Wilm, U. Norinder, M. Agea, Christina de Bruyn Kops, Conrad Stork, J. Kühnl, J. Kirchmair (2020)
> 46. [Conformal prediction based active learning by linear regression optimization](https://www.sciencedirect.com/science/article/abs/pii/S0925231220300461?via%3Dihub) by Sergio Matiz, Kenneth E.Barner (2020)
> 47. [Conformal prediction intervals for the individual treatment effect](https://arxiv.org/pdf/2006.01474.pdf) by Danijel Kivaranovic, Robin Ristl, Martin Poschb, Hannes Leeb (2020)
> 48. [Nearest neighbor based conformal prediction](https://www.f08.uni-stuttgart.de/.content/media/downloads/Mathematik/mathematische_berichte/2020/2020-002.pdf) by László Györfi and Harro Walk (2020)
> 49. [Concepts and Applications of Conformal Prediction in Computational Drug Discovery](https://arxiv.org/pdf/1908.03569.pdf) by Isidro Cortés-Ciriano and Andreas Bender (2019) 🔥
> 50. [Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project](https://academic.oup.com/mutage/article/34/1/33/5239859) by Ulf Norinder, Ernst Ahlberg, Lars Carlsson (2018)
> 51. [Nested Conformal Prediction and the Generalized Jackknife](https://arxiv.org/pdf/1910.10562v1.pdf) by Arun Kuchibhotla and Aaditya Ramdas (Carnegie Mellon, 2019)
> 52. [Predictive inference with the jackknife+](https://arxiv.org/abs/1905.02928) by Rina Foygel Barber, Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani (2020) 🔥🔥🔥🔥🔥
> 53. [Nonparametric predictive distributions based on conformal prediction](https://link.springer.com/article/10.1007%2Fs10994-018-5755-8) by Vladimir Vovk, Jieli Shen, Valery Manokhin and Min-ge Xie (Royal Holloway, UK, Rutgers, USA, 2018) 🔥
> 54. [A Distribution-Free Test of Covariate Shift Using Conformal Prediction](https://arxiv.org/pdf/2010.07147.pdf) by Xiaoyu Hu and Jing Lei (Peking Univerity, China and Carnegie Mellon, USA, 2020) 🔥
> 55. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
> 56. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (2020)
> 57. [Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction](https://pubmed.ncbi.nlm.nih.gov/30908915/) by Ulf Norinder, Fredrik Svensson
> 58. [Conformal prediction of HDAC inhibitors](https://www.tandfonline.com/doi/abs/10.1080/1062936X.2019.1591503?journalCode=gsar20) by U. Norinder, J.J.Navaka, E. Lopez-Lopez, D. Mucs & J.L. Medina-Franco (2019)
> 59. [Computing Full Conformal Prediction Set with Approximate Homotopy](https://arxiv.org/pdf/1909.09365.pdf) by Eugene Ndiaye, Ichiro Takeuchi (2019)
> 60. [Conformal Prediction Based on Raman Spectra for the Classification of Chinese Liquors](https://journals.sagepub.com/doi/10.1177/0003702819831017) by Jiao Gu, Huaibo Liu, Chaoqun Ma, Lei Li, Chun Zhu, Christ Glorieux, Guoqing Chen (2019)
> 61. [Efficient and minimal length parametric conformal prediction regions](https://export.arxiv.org/pdf/1905.03657) by Daniel Eck and Forrest Crawford (2019)
> 62. [Conformal Prediction for Students’ Grades in a Course Recommender System](http://proceedings.mlr.press/v105/morsomme19a/morsomme19a.pdf) by Raphael Morsomme and Evgueni Smirnov (2019)
> 63. [Efficient iterative virtual screening with Apache Spark and conformal prediction](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0265-z) by Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure and Ola Spjuth (2018)
> 64. [Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction](https://pubmed.ncbi.nlm.nih.gov/30459617/) by Samuel Lampa, Jonathan Alvarsson, Staffan Arvidsson Mc Shane, Arvid Berg, Ernst Ahlberg, Ola Spjuth (2018)
> 65. [Maximizing gain in high-throughput screening using conformal prediction](https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-018-0260-4.pdf) by Fredrik Svensson, Avid M. Afzal1, Ulf Norinder and Andreas Bender (2018)
> 66. [Conformalized Survival Analysis](https://arxiv.org/pdf/2103.09763.pdf) by Emmanuel Candès, Lihua Lei and Zhimei Ren (2021) [R-Code](https://github.com/valeman/cfsurv_paper) 🔥
> 67. [Random Forest Prediction Intervals](https://haozhestat.github.io/files/manuscript_RFIntervals_FinalVersion.pdf) by Haozhe Zhang†, Joshua Zimmerman†, Dan Nettleton† and Daniel J. Nordman† (Iowa State University, USA, 2019)
> 68. [Conformal Training: Learning Optimal Conformal Classifiers | DeepMind](https://arxiv.org/pdf/2110.09192.pdf) by David Stutz (DeepMind), Krishnamurthy Dvijotham, Ali Taylan Cemgil and Arnaud Doucet (2021)
> 69. [Comparing the Bayes and typicalness frameworks](https://link.springer.com/content/pdf/10.1007%2F3-540-44795-4_31.pdf) by Thomas Melluish, Craig Saunders, Ilia Nouretdinov, and Volodya Vovk (Royal Holloway, UK, 2001). 🔥
> 70. [Large-scale probabilistic predictors with and without guarantees of validity](https://papers.nips.cc/paper/2015/file/a9a1d5317a33ae8cef33961c34144f84-Paper.pdf) by Vladimir Vovk, Ivan Petej, and Valentina Fedorova (Royal Holloway, Yandex, NeurIPS) 🔥🔥🔥🔥🔥
> 71. [Inductive conformal prediction for silent speech recognition](https://iopscience.iop.org/article/10.1088/1741-2552/ab7ba0) by Ming Zhang, You Wang, Wei Zhang, Meng Yang, Zhiyuan Luo and Guang Li (2020)
> 72. [Conformal Prediction using Conditional Histograms](https://papers.nips.cc/paper/2021/file/31b3b31a1c2f8a370206f111127c0dbd-Paper.pdf) by Matteo Sesia and Yaniv Romano (NeurIPS 2021 paper). [code](https://github.com/msesia/chr) 🔥
> 73. [Valid prediction intervals for regression problems](https://arxiv.org/abs/2107.00363) by Nicolas Dewolf, Bernard De Baets, Willem Waegeman (2021) 🔥
> 74. [Application of conformal prediction interval estimations to market makers’ net positions](http://proceedings.mlr.press/v128/wisniewski20a.html) by Wojciech Wisniewski, David Lindsay, Sian Lindsay (Royal Holloway, UK, 2020)
> 75. [Locally Valid and Discriminative Prediction Intervals for Deep Learning Models](https://papers.nips.cc/paper/2021/hash/46c7cb50b373877fb2f8d5c4517bb969-Abstract.html) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (NeurIPS, 2021) 🔥
> 76. [Distribution-Free Federated Learning with Conformal Predictions](https://arxiv.org/pdf/2110.07661.pdf) by Charles Lu and Jayashree Kalpathy-Cramer (2022)
> 77. [Coreset-based Conformal Prediction for Large-scale Learning](https://proceedings.mlr.press/v105/riquelme-granada19a.html) by Nery Riquelme-Granada, Khuong Nguyen, Zhiyuan Luo (Royal Holloway, UK, 2019)
> 78. [Fast probabilistic prediction for kernel SVM via enclosing balls](https://proceedings.mlr.press/v128/riquelme-granada20a.html) by Nery Riquelme-Granada, Khuong Nguyen, Zhiyuan Luo (Royal Holloway, UK, 2020)
> 79. [Conformalized density- and distance-based anomaly detection in time-series data](https://arxiv.org/abs/1608.04585) by Evgeny Burnaev, Vladislav Ishimtsev (2016)
> 80. [Predictive Inference with Weak Supervision](https://arxiv.org/pdf/2201.08315.pdf) by Maxime Cauchois, Suyash Gupta, Alnur Ali and John Duchi (Stanford, 2022)
> 81. [Conformal Prediction in Clinical Medical Sciences](https://link.springer.com/10.1007/s41666-021-00113-8) by Janette Vazquez and Julio C. Facelli University of Utah, 2022)
> 82. [Provably Improving Expert Predictions with Conformal Prediction](https://arxiv.org/pdf/2201.12006.pdf) by Eleni Straitouri, Lequng Wang, Nastaran Okati and Manuel Gomez Rodriguez (Max Planck Institute for Software Systems / Cornell University, 2021).
> 83. [Conformal predictive distributions with kernels](https://arxiv.org/abs/1710.08894) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Royal Holloway, UK, 2017)
> 84. [Multi-class probabilistic classification using inductive and cross Venn–Abers predictors](https://proceedings.mlr.press/v60/manokhin17a.html) by Valery Manokhin (Royal Holloway, UK, 2017). 🔥
> 85. [Computationally efficient versions of conformal predictive distributions](https://arxiv.org/abs/1911.00941) by Vladimir Vovk, Ivan Petej, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Royal Holloway, UK, 2019).
> 86. [Cover your cough: detection of respiratory events with confidence using a smartwatch](https://proceedings.mlr.press/v91/nguyen18a.html) by Khuong An Nguyen, Zhiyuan Luo (Royal Holloway, 2019).
> 87. [Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction](https://www.tandfonline.com/doi/full/10.1080/23270012.2022.2031324) by Ulf Norinder and Petra Norinder (2022)
> 88. [Conformal Prediction for the Design Problem](https://arxiv.org/pdf/2202.03613.pdf) by Clara Fannjianga, Stephen Batesa, Anastasios Angelopoulosa, Jennifer Listgartena and Michael I. Jordan (Berkeley, 2022) 🔥
> 89. [Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging](https://arxiv.org/pdf/2202.05265.pdf) by Anastasios N. Angelopoulos, Amit Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano (Berkeley and Technion, 2022) 🔥🔥🔥🔥🔥
> 90. [Conformal predictive decision making](http://proceedings.mlr.press/v91/vovk18b/vovk18b.pdf) by Vladimir Vovk and Claus Bendtsen (2018).
> 91. [The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting](https://arxiv.org/pdf/2202.04166.pdf) by Alnur Ali1, Maxime Cauchois and John C. Duchi (Stanford, 2022)
> 92. [E-values: Calibration, combination, and applications](https://arxiv.org/pdf/1912.06116.pdf) by Vladimir Vovk (Royal Holloway) and Ruodu Wang (University of Waterloo) (2019)
> 93. [Conformal Prediction Sets with Limited False Positives](https://arxiv.org/pdf/2202.07650.pdf) by Adam Fisch, Tal Schuster, Tommi Jaakkola and Regina Barzilay [code](https://github.com/ajfisch/conformal-fp) (MIT / Google Research, 2022) 🔥
> 94. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/pdf/2202.07282.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (EDF / INRIA / CMAP, France, 2022) 🔥 [Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
> 95. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2202.08756v1.pdf) by Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen (Arctic University of Norway, 2022) TIME SERIES 🚀🔥 [Python Code](https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression)
> 96. [Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors](http://proceedings.mlr.press/v60/arvidsson17a/arvidsson17a.pdf) by Staffan Arvidsson, Ola Spjuth, Lars Carlsson and Paolo Toccaceli (University of Uppsala, Astra Zeneca, Royal Holloway, 2017)
> 97. [Probabilistic Prediction in scikit-learn](https://www.diva-portal.org/smash/get/diva2:1603345/FULLTEXT01.pdf) by Sweidan, Dirar and Ulf Johansson. 🔥
> 98. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://arxiv.org/pdf/2205.09095.pdf) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🚀🔥
> 99. [Valid model-free spatial prediction](https://arxiv.org/pdf/2006.15640.pdf) by Huiying Mao, Ryan Martin and Brian J Reich (2020)
> 100. [Conformal Prediction with Temporal Quantile Adjustments](https://arxiv.org/pdf/2205.09940.pdf) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (2022) TIME SERIES 🚀🔥
> 101. [Calibration of Natural Language Understanding Models with Venn–ABERS Predictors](https://arxiv.org/pdf/2205.10586.pdf) by Patrizio Giovannotti (Royal Holloway, UK, 2022) NLP
> 102. [Conformal prediction interval for dynamic time-series](https://proceedings.mlr.press/v139/xu21h.html) by Chen Xu, Yao Xie (Georgia Tech, 2021) TIME SERIES 🚀🔥
> 103. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
> 104. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) TIME SERIES 🚀🔥[Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
> 105. [Conformal Time-Series Forecasting](https://proceedings.neurips.cc/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf) by Kamile Stankeviciu te and Ahmed M. Alaa (2021) TIME SERIES 🚀🔥
> 106. [Efficient Conformal Prediction via cascaded inference with expanded admission](https://arxiv.org/pdf/2007.03114.pdf) by Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay (MIT 2021]. [Python Code](https://github.com/ajfisch/conformal-cascades)
> 107. [Split Localized Conformal Prediction](https://arxiv.org/abs/2206.13092) by Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu (University of Texas, 2022). [Python Code](https://github.com/valeman/SLCP)
> 108. [Three Applications of Conformal Prediction for Rating Breast Density in Mammography](https://arxiv.org/pdf/2206.12008.pdf) by Charles Lu, Ken Chang, Praveer Singh, Jayashree Kalpathy-Crame (2022)
> 109. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) Python Code TIME SERIES 🚀🔥
> 110. [Recommendation systems with distribution-free reliability guarantees](https://arxiv.org/pdf/2207.01609.pdf)) by Anastasious Angelopolous, Karl Krauth, Stephen Bates, Yixin Wang and Michael I. Jordan (Berkeley 2022)
> 111. [Model Agnostic Conformal Hyperparameter Optimization](https://arxiv.org/pdf/2207.03017.pdf) by Riccardo Doyle (Spotify, 2022)
> 112. [Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets](https://arxiv.org/pdf/2207.02238.pdf) by Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz (2022) [code](https://github.com/clu5/lumbar-conformal) 🔥
> 113. [Conformal Off-Policy Prediction in Contextual Bandits](https://arxiv.org/pdf/2206.04405.pdf) by Muhammad Faaiz Taufiq, Jean-François Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet (Oxford, 2022) [Video presentation](https://youtu.be/K5RAjP1Ze30)
> 114. [Training Uncertainty-Aware Classifiers with Conformalized Deep Learning](https://arxiv.org/abs/2205.05878) by Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou (Technion, USCLA 2022) [Video presentation](https://www.youtube.com/watch?v=RJ7ShciZq2s); [Code](https://github.com/bat-sheva/conformal-learning)
> 115. [Semantic uncertainty intervals for disentangled latent space](https://arxiv.org/pdf/2207.10074.pdf) by Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, and Phillip Isola (Unversity of Berkeley, Technion, 2022) 🔥
> 116. [MAPIE: an open-source library for distribution-free uncertainty quantification](https://arxiv.org/pdf/2207.12274.pdf) by Vianney Taquet, Vincent Blot, Thomas Morzadec, Louis Lacombe, Nicolas Brunel (Quantmetry, France, 2022)
> 117. [CODiT: Conformal Out-of-Distribution Detection in Time- Series Data](https://arxiv.org/pdf/2207.11769.pdf) by Ramneet Kaur et.al., Unibersity of Pensylvania (2022). [Code](https://github.com/kaustubhsridhar/time-series-OOD) TIME SERIES 🚀🔥
> 118. [Confident Adaptive Language Modeling](https://arxiv.org/pdf/2207.07061.pdf) by Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler (Google, MIT, 2022j
> 119. [Probabilistic Conformal Prediction Using Conditional Random Samples](https://arxiv.org/pdf/2206.06584.pdf) by Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei (Columbia University, 2020) [Code](https://github.com/Zhendong-Wang/Probabilistic-Conformal-Prediction)
> 120. [A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tome and Jose Moreira (University of Aveiro, 2022) TIME SERIES 🚀🔥
> 121. [A novel Deep Learning approach for one-step Conformal Prediction approximation](https://arxiv.org/pdf/2207.12377.pdf) by Julia A. Meister, Khuong An Nguyen, Stelios Kapetanakis and Zhiyuan Luo (University of Brighton, UK, 2022) 🔥
> 122. [Conformal Risk Control](https://arxiv.org/pdf/2208.02814.pdf) by Anastasious Angelopolous, Stephen Bates, Adam Fisch, Lihua Lei and Tal Schuster (Berkeley, Stanford, MIT and Google Research, 2022) 🔥
> 123. [CD-split and HPD-split: Efficient Conformal Regions in High Dimensions](https://www.jmlr.org/papers/volume23/20-797/20-797.pdf) by Rafael Izbicki, Gilson Shimizu, Rafael B. Stern (San Carlos University Brazil, 2022)
> 124. [Flexible distribution-free conditional predictive bands using density estimators](https://proceedings.mlr.press/v108/izbicki20a.html) by Rafael Izbicki, Gilson Shimizu, and Rafael B. Stern (San Carlos University Brazil, 2020)
> 125. [Split Conformal Prediction for Dependent Data](https://arxiv.org/abs/2203.15885) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano (IMPA, Rio de Janeiro, Brazil, 2022)
> 126. [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://arxiv.org/pdf/2208.08401.pdf) by Isaac Gibbs and Emmanual Candes (Stanford, 2022) 🔥
> 127. [A General Framework For Multi-step Ahead Adaptive Conformal Heteroscedastic Time Series Forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tomé, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES 🚀🔥
> 128. [Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning](https://copa-conference.com/papers/COPA2022_paper_13.pdf) by Alice E. Ashby, Julia A. Meister, Khuong An Nguyen, Zhiyuan Luo, Werner Gentzk (University of Brighton, 2022)
> 129. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/papers/COPA2022_paper_10.pdf) by Amr Alkhatib, Henrik Bostroem and Ulf Johansson (2022)
> 130. [Conformal prediction for hypersonic flight vehicle classification](https://www.copa-conference.com/papers/COPA2022_paper_1.pdf) by Zepu Xi, Xuebin Zhuang, Hongbo Chen (Yat-sen University, Guangzhou, China, 2022) [Slides](https://copa-conference.com/presentations/Zepu.pdf)
> 131. [Robust Gas Demand Forecasting With Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
> 132. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath and Florian Ziel (2020)
> 133. [Conformal Prediciton beyond exchangeability](https://arxiv.org/abs/2202.13415) by Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani (2022)
> 134. [Robust Gas Demand Forecasting with Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
> 135. [Split conformal prediction for dependant data](https://arxiv.org/pdf/2203.15885.pdf) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos and João Vitor Romano (2022)
> 136. [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://arxiv.org/pdf/2208.11111.pdf) by Ziyi Liang, Matteo Sesia, Wenguang Sun (UCLA, 2022)
> 137. [Conformal Prediction Bands for Two-Dimensional Functional Time Series](https://arxiv.org/pdf/2207.13656.pdf) by Niccolo` Ajroldia, Jacopo Diquigiovannib, Matteo Fontanac, Simone Vantinia (2022)
> 138. [Conformal prediction of small-molecule drug resistance in cancer cell lines](https://copa-conference.com/papers/COPA2022_paper_22.pdf) by Saiveth Hernandez-Hernandez, Sachin Vishwakarma and Pedro Ballester (2022)
> 139. [Ellipsoidal conformal inference for Multi-Target Regression](https://copa-conference.com/papers/COPA2022_paper_7.pdf) by Soundouss Messoudi, Sebastien Destercke, Sylvain Rousseau (2022) [Slides](https://copa-conference.com/presentations/soundouss.pdf)
> 140. [Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey](https://arxiv.org/abs/2209.03580) by Sophia Sun (UCLA, 2022)
> 141. [Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9103229) by Håkan Wieslander , Philip J. Harrison, Gabriel Skogberg, Sonya Jackson, Markus Fridén, Johan Karlsson, Ola Spjuth, and Carolina Wählby (2021)
> 142. [Audio–visual domain adaptation using conditional semi-supervised Generative Adversarial Networks](https://www.sciencedirect.com/science/article/pii/S0925231219316170) by Christos Athanasiadis, Enrique Hortal, Stylianos Asteriadis (2022)
> 143. [Conformal Prediction is Robust to Label Noise](https://arxiv.org/pdf/2209.14295.pdf) by Bat-Sheva Einbinder, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano (2022)
> 144. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🔥
> 145. [Batch Multivalid Conformal Prediction](https://arxiv.org/abs/2209.15145) by Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth (Stanford, university of Pensylvania, 2022)
> 146. [Selection by Prediction with Conformal p-values](https://arxiv.org/pdf/2210.01408.pdf) by Ying Jin1 and Emmanuel J. Candes, (Stanford, 2022) [Video](https://www.youtube.com/watch?v=YEpvHsHCCvQ) [code](https://github.com/ying531/selcf_paper)
> 147. [Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction](https://arxiv.org/pdf/2210.03067v1.pdf) by Sangwoo Park, Kfir M. Cohen, Osvaldo Simeone (2022)
> 148. [Conformalized Fairness via Quantile Regression](https://web7.arxiv.org/pdf/2210.02015.pdf) by Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang (University of Alberta, Noah Arc Huawei, 2022)
> 149. [Test-time recalibration of conformal predictors under distribution shift based on unlabeled examples](https://arxiv.org/pdf/2210.04166.pdf) by Fatih Furkan Yilmaz, and Reinhard Heckel (Rice University / University of Munuch, 2022)
> 150. [Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains](https://arxiv.org/abs/2210.02271) by Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh Halappanavar (2022)
> 151. [Predictive inference with feature conformal prediction](https://arxiv.org/pdf/2210.00173.pdf) by Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan (Tsinghua University, Mila - Quebec AI Institute, Shanghai Artificial Intelligence Laboratory, Shanghai Qi Zhi Institute, 2022) [code](https://github.com/AlvinWen428/FeatureCP) 🔥
> 152. [Constructing Prediction Intervals with Neural Networks: An Empirical Evaluation of Bootstrapping and Conformal Inference Methods](https://arxiv.org/pdf/2210.05354.pdf) by Alex Contarino, Christine Schubert Kabban, Chancellor Johnstone and Fairul Mohd-Zaid (2022)
> 153. [Spatio-Temporal Wildfire Prediction using Multi-Modal Data](https://arxiv.org/pdf/2207.13250.pdf) by Chen Xu1, Yao Xie, Daniel A. Zuniga Vazquez, Rui Yao, and Feng Qiu (2022)
> 154. [Calibrating AI models for few-shot demodulation via conformal prediction](https://arxiv.org/pdf/2210.04621.pdf) Kfir M. Cohen1, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai (2022)
> 155. [Test-time recalibration of conformal predictors under distribution shift based on unlabeled examples](https://arxiv.org/pdf/2210.04166.pdf) [Code](https://github.com/MLI-lab/recalibrating_conformal_prediction)
> 156. [Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction](https://arxiv.org/pdf/2210.10161.pdf) by Wenlu Tang, Guohao Shen, Yuanyuan Lin and Jian Huang (The Hong Kong Polytechnic University, 2022)
> 157. [Safe Planning in Dynamic Environments using Conformal Prediction](https://arxiv.org/pdf/2210.10254.pdf) by Lars Lindemann, Matthew Cleaveland∗, Gihyun Shim, and George J. Pappas (University of Pensylvania, 2022)
> 158. [Conformal prediction under feedback covariate shift for biomolecular design](https://www.pnas.org/doi/10.1073/pnas.2204569119) by Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos,and Michael I. Jordan (2022) 🔥
> 159. [Conformal Predictor for Improving Zero-shot Text Classification Efficiency](https://arxiv.org/pdf/2210.12619.pdf) by Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu, Nazneen Rajani (Saleforce AI Research and Hugging Face, 2022)
> 160. [Bayesian Optimization with Conformal Coverage Guarantees](https://arxiv.org/pdf/2210.12496.pdf) by Samuel Stanton, Wesley Maddox and Andrew Gordon Wilson (Genentech, New York University, 2022) [Code](https://github.com/samuelstanton/conformal-bayesopt) 🔥
> 161. [Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability](https://arxiv.org/pdf/2210.16049.pdf) by Ibai Lanaa, Ignacio (In ̃aki) Olabarrietaa, Javier Del Sera (2022)
> 162. [Training Uncertainty-Aware Classifiers with Conformalized Deep Learning](https://openreview.net/pdf?id=NaZwgxp-mT_) by Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia and Yanfei Zhou (Technion/UCLA, NeurIPS 2022 paper) 🔥
> 163. [Conformalized Fairness via Quantile Regression](https://openreview.net/pdf?id=rwyISFoSmXd) by Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang (University of Alberta, Huawei Noah’s Ark Lab Canada, NeurIPS 2022 paper) [Code](https://github.com/Lei-Ding07/Conformal_Quantile_Fairness) 🔥
> 164. [Engineering Uncertainty Representations to Monitor Distribution Shifts](https://openreview.net/pdf?id=Koug1i2HpH) by Thomas Bonnier and Benjamin Bosch (Société Générale, 2022)
> 165. [CONffusion: CONFIDENCE INTERVALS FOR DIFFUSION MODELS](https://arxiv.org/pdf/2211.09795.pdf) [Project](https://www.vision.huji.ac.il/conffusion/)[Code](https://github.com/eliahuhorwitz/Conffusion) by Eliahu Horwitz, Yedid Hoshen (Hebrew University of Jerusalem, 2022) 🔥
> 166. [Semantic uncertainty intervals for disentangled latent spaces](https://swamiviv.github.io/semantic_uncertainty_intervals/) by Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola (MIT, Berkeley, Technion, 2022) 🔥
> 167. [But are you sure? An uncertainty-aware perspective on explainable AI](https://assets.amazon.science/bf/d0/14c3eb614699909dae8a092b9492/but-are-you-sure-an-uncertainty-aware-perspective-on-explainable-ai.pdf) by Charlie Marx, Youngsuk Park, Hilaf Hasson, Yuyang (Bernie) Wang, Stefano Ermon, Jun Huan (2022)
> 168. [Calibrating AI Models for Wireless Communications via Conformal Prediction](https://arxiv.org/pdf/2212.07775.pdf) by Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone and Shlomo Shamai (2022)
> 169. [Predicting Endocrine Disruption Using Conformal Prediction – A Prioritization Strategy to Identify Hazardous Chemicals with Confidence](https://pubs.acs.org/doi/10.1021/acs.chemrestox.2c00267) by Maria Sapounidou, Ulf Norinder and Patrick Andersson (2022)
> 170. [Conformal Loss-Controlling Prediction](https://arxiv.org/pdf/2301.02424.pdf) by Di Wang, Ping Wang, Zhong Ji, Xiaojun Yang, Hongyue Li (2023)
> 171. [ROBUST AND SCALABLE UNCERTAINTY ESTIMATION WITH CONFORMAL PREDICTION FOR MACHINE-LEARNED INTERATOMIC POTENTIALS](https://arxiv.org/pdf/2208.08337.pdf) [Code](https://github.com/valeman/conformal_prediction_in_latent_space) by Yuge Hu, Joseph Musielewicz, Zachary Ulissi, Andrew J. Medford (Georgia Institute of Technology/Carnegie Mellon University, 2022)
> 172. [Clustering of Trajectories using Non-Parametric Conformal DBSCAN Algorithm](https://sites.rutgers.edu/jie-gao/wp-content/uploads/sites/375/2022/03/conformal-DBSCAN-compressed.pdf) by Haotian Wang, Jie Gao, Min-ge Xie Rutgers University, 2022)
> 173. [But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI](https://assets.amazon.science/bf/d0/14c3eb614699909dae8a092b9492/but-are-you-sure-an-uncertainty-aware-perspective-on-explainable-ai.pdf) by Charlie Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Jun Huan (2022)
> 174. [Prediction-Powered Inference](https://arxiv.org/pdf/2301.09633.pdf) by Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic (Universify of Berkeley, 2022) [code](https://github.com/aangelopoulos/ppi_py) 🔥
> 175. [Conformal Prediction for Trustworthy Detection of Railway Signals](https://arxiv.org/pdf/2301.11136.pdf) by Leo Andeol, Thomas Fel, Florence de Grancey, Luca Mossina (Institute de Mathematiques de Toulouse, SCNF, 2022)
> 176. [Conformal inference is (almost) free for neural networks trained with early stopping](https://arxiv.org/pdf/2301.11556.pdf) by Ziyi Liang, Yanfei Zhou†, Matteo Sesia (University of Southern California, 2022)
> 177. [PAC Prediction Sets for Large Language Models of Code](https://www.seas.upenn.edu/~akhakhar/pacsetllm.pdf) by Adam Khakhar, Stephen Mell, Osbert Bastani (University of Pennsylvania, 2023)
> 178. [Physics Constrained Motion Prediction with Uncertainty Quantification](https://arxiv.org/pdf/2302.01060.pdf) by Renukanandan Tumu, Lars Lindemann†, Truong Nghiem, Rahul Mangharam, (2023)
> 179. [Accelerating difficulty estimation for conformal regression forests](https://link.springer.com/content/pdf/10.1007/s10472-017-9539-9.pdf) by Henrik Bostroem, Henrik Linusson, Tuve Loefstroem, Ulf Johansson (2017)
> 180. [Conformal prediction for exponential families and generalized linear models](https://arxiv.org/pdf/1905.03657v1.pdf)
> 181. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) by Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023) [Code](https://github.com/Sulam-Group/k-rcps)
> 182. [Localized Conformal Prediction: A Generalized Inference Framework to Conformal Prediction](https://arxiv.org/pdf/2106.08460.pdf) [Code](https://github.com/LeyingGuan/LCPexperiments) [R package](https://github.com/LeyingGuan/LCP)
> 183. [From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling](https://arxiv.org/pdf/2302.05304.pdf) by Ernsting et.al. (2023)
> 184. [Conformal prediction for STL runtime verification](https://arxiv.org/pdf/2211.01539.pdf) by Lars Lindemann, Xin Qin, Jyotirmoy V. Deshmukh, George J. Pappas (University of Pennsylvania/University of Southern California, 2022)
> 185. [Adaptive Conformal Prediction for Motion Planning among Dynamic Agents](https://arxiv.org/pdf/2212.00278.pdf) by Anushri Dixit, Lars Lindemann, Skylar Wei, Matthew Cleaveland, George J Pappas, Joel W Burdick (California Institute of Technology/University of Pennsylvania, 2022)
> 186. [Classification with Valid and Adaptive Coverage](https://proceedings.neurips.cc/paper/2020/file/244edd7e85dc81602b7615cd705545f5-Paper.pdf) [Code](https://github.com/msesia/arc) by Yaniv Romano, Matteo Sesia, Emmanuel Candes (Neurips, 2020) 🔥
> 187. [Risk Control for Online Learning Models](https://openreview.net/forum?id=uqLDy0HGPR7) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (2023)
> 188. [Derandomized novelty detection with FDR control via conformal e-values](https://arxiv.org/pdf/2302.07294v1.pdf) by Meshi Bashari, Amir Epstein, Yaniv Romano, and Matteo Sesia (2023) [code](https://github.com/Meshiba/derandomized-novelty-detection)
> 189. [Sensititivty analysis of individual treatment effects: A robust conformal inference approach](https://www.pnas.org/doi/epdf/10.1073/pnas.2214889120) [Code](https://github.com/ying531/cfsensitivity_paper) by Ying Jin, Zhimei Ren and Emmanual Candes (2023)
> 190. [Improving Adaptive Conformal Prediction Using Self-Supervised Learning](https://arxiv.org/abs/2302.12238) by Nabeel Seedat, Alan Jeffares, Fergus Imrie and Mihaela van der Schaar (Cambridge, 2023) [Video](https://www.youtube.com/watch?v=pagxatn7xNw) [Code](https://github.com/seedatnabeel/SSCP)
> 191. [Learning by Transduction - of the of earliest conformal prediction papers] ([https://dl.acm.org/doi/10.5555/2074094.2074112#sec-comments](https://dl.acm.org/doi/10.5555/2074094.2074112#sec-comments)) by Alex Gammerman, Vladimir Vovk and Vladimir Vapnik (Royal Holloway, University of London, 1998) 🔥
> 192. [Hedging Predictions in Machine Learning](https://arxiv.org/pdf/cs/0611011.pdf) by Alexander Gammerman and Vladimir Vovk (2008) 🔥🔥🔥🔥🔥
> 193. [Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction](https://www.mdpi.com/2218-273X/8/3/85) by Ulf Norinder, Glenn Myatt and Ernst Ahlberg
> 194. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) TIME SERIES 🚀🔥
> 195. [Fortuna: A Library for Uncertainty Quantification in Deep Learning](https://arxiv.org/abs/2302.04019) by Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau (2023)
> 196. [Conformal inference is (almost) free for neural networks trained with early stopping](https://arxiv.org/abs/2301.11556) by Ziyi Liang, Yanfei Zhou, Matteo Sesia (2023)
> 197. [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://arxiv.org/pdf/2212.03710.pdf) by Mahmoud Shoush and Marlon Dumas (University of Tartu,2022)
> 198. [Machine-Learning Applications of Algorithmic Randomness](https://eprints.soton.ac.uk/258960/1/Random_ICML99.pdf) by Vladimir Vovk, Alex Gammerman and Craig Saunders (1999) 🔥
> 199. [On the universal distribution of the coverage in split conformal prediction](https://arxiv.org/abs/2303.02770) by Paulo C. Marques F. (2023)
> 200. [Lightweight, Uncertainty-Aware Conformalized Visual Odometry](https://arxiv.org/pdf/2303.02207.pdf) by Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula, and Amit Ranjan Trivedi (University of Illinois Chicago, 2023)
> 201. [Group conditional validity via multi-group learning](https://arxiv.org/abs/2303.03995v1) by Samuel Deng, Navid Ardeshir, Daniel Hsu (Columbia University, 2023)
> 202. [Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis](https://arxiv.org/pdf/2303.10694.pdf) by Subhankar Ghosh, Taha Belkhouj, Yan Yan, Janardhan Rao Doppa (Washington State University, 2023)
> 203. [Mondrian conformal regressors](https://proceedings.mlr.press/v128/bostrom20a.html) by Henrik Boström, Ulf Johansson (2020)
> 204. [Mondrian Conformal Predictive Distributions](https://proceedings.mlr.press/v152/bostrom21a/bostrom21a.pdf) by Henrik Boström, Ulf Johansson, Tuwe Löfström (2021) 🔥
> 205. [Adaptive Conformal Prediction by Reweighting Nonconformity Score](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou, Nicolas J.B Brunel (2023) [Code](https://github.com/salimamoukou/ACPI)
> 206. [Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation](https://arxiv.org/pdf/2303.12246.pdf) by Heng Yang and Marco Pavone (NVIDIA, 2023)
> 207. [A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum](https://arxiv.org/pdf/2010.07147.pdf) by Xiaoyu Hu and Jing Lei (Peking University and Carnegie Mellon University, 2023)
> 208. [Conformalized Semi-Supervised Random Forest For Classification and Abnormality Detection](https://arxiv.org/abs/2302.02237) (2023)
> 209. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023)
> 210. [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://arxiv.org/pdf/2304.00194.pdf) by Shuo Yang, George J. Pappas, Rahul Mangharam, and Lars Lindemann (University of Pennsylvania, 2023)
> 211. [Conformal Prediction Regions for Time Series using Linear Complementarity Programming](https://arxiv.org/pdf/2304.01075.pdf) by Matthew Cleaveland, Insup Lee, George J. Pappas†, and Lars Lindemann (University of Pennsylvania, 2023)
> 212. [Development and Evaluation of Conformal Prediction Methods for QSAR](https://arxiv.org/pdf/2304.00970.pdf) by Yuting Xua, Andy Liawa, Robert P. Sheridan (Merck, 2023)
> 213. [Multi-Agent Reachability Calibration with Conformal Prediction](https://arxiv.org/pdf/2304.00432v1.pdf) by Anish Muthali1, Haotian Shen, Sampada Deglurkar, Michael H. Lim, Rebecca Roelofs, Aleksandra Faust, Claire Tomlin (University of Berkeley, 2023)
> 214. [Conformalized Unconditional Quantile Regression](https://arxiv.org/pdf/2304.01426.pdf) by Ahmed M. Alaa, Zeshan Hussain, David Sontag (Berkeley/MIT, 2023).
> 215. [Conformal Off-Policy Evaluation in Markov Decision Processes](https://arxiv.org/pdf/2304.02574v1.pdf) by Daniele Foffano, Alessio Russo and Alexandre Proutiere (KTH, 2023) 🔥🌟
> 216. [Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning](https://arxiv.org/pdf/2304.03398.pdf) by Sangwoo Park and Osvaldo Simeone (2023) [Code](https://github.com/kclip/quantum-CP) QuantumML 🚀🔥
> 217. [Probabilistic prediction with locally weighted jackknife predictive system](https://link.springer.com/content/pdf/10.1007/s40747-023-01044-0.pdf#page18) by Di Wang, Ping Wang, Pingping Wang, Cong Wang, Zhen He, Wei Zhang 🔥
> 218. [Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision](https://arxiv.org/abs/2304.06158) by Siddhaarth Sarkar, Arun Kumar Kuchibhotla (2023)
> 219. [Conformal Regression in Calorie Prediction for Team Jumbo-Visma](https://arxiv.org/abs/2304.03778) by Kristian van Kuijk, Mark Dirksen, Christof Seiler (2023) 🔥[slides](https://copa-conference.com/presentations/Kristian.pdf)
> 220. [Design-based conformal prediction](https://arxiv.org/pdf/2303.01422.pdf) by Jerzy Wieczorek (2023) [code](https://github.com/ColbyStatSvyRsch/surveyConformal-paper-code) 🌟
> 221. [Inductive Confidence Machines for Regression](https://link.springer.com/content/pdf/10.1007/3-540-36755-1_29.pdf) by Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman (2002) 🔥📚
> 222. [Model-Agnostic Nonconformity Functionsfor Conformal Classification](https://ieeexplore.ieee.org/document/7966105) by Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström (2017) 🔥📚
> 223. [Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study](https://proceedings.mlr.press/v152/aleksandrova21a.html) by Marharyta Aleksandrova, Oleg Chertov (2021) 🔥📚
> 224. [Inductive Conformal Prediciton: A Straightforward Introduction with examples in Python](https://arxiv.org/pdf/2206.11810.pdf) by Martim Sousa (2022) [Code](https://github.com/Quilograma/ConformalPredictionTutorial/blob/main/Conformal%20Prediction.ipynb) 🔥📚
> 225. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://stanfordasl.github.io/wp-content/papercite-data/pdf/Sinha.Pavone.CDC23.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Standord, 2023)
> 226. [Calibrated Explanations: with Uncertainty Information and Counterfactuals](https://arxiv.org/pdf/2305.02305.pdf) by Helena Löfström, Tuwe Löfström, Ulf Johansson, Cecilia S ̈onstr ̈od (2023) [Code](https://github.com/Moffran/calibrated_explanations) 🔥
> 227. [Optimizing Hyperparameters with Conformal Quantile Regression](https://arxiv.org/pdf/2305.03623.pdf) by David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau (Amazon Science, 2023) 🔥
> 228. [Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens](https://chemrxiv.org/engage/chemrxiv/article-details/6456778807c3f02937503688) by Andreas Luttens, Israel Cabeza de Vaca, Leonard Sparring, Ulf Norinder, Jens Carlsson (2023) [Code](https://github.com/carlssonlab/conformalpredictor) [Datasets](https://zenodo.org/record/7903161) 🔥
> 229. [Predicting skin sensitizers with confidence — Using conformal prediction to determine applicability domain of GARD](https://www.sciencedirect.com/science/article/pii/S0887233318300237?via%3Dihub) by Andy Forreryd, Ulf Norinder, Tim Lindberg, Malin Lindstedt (2018) 📚
> 230. [Confidence-based Prediction of Antibiotic Resistance at the Patient-level Using Transformers](https://www.biorxiv.org/content/10.1101/2023.05.09.539832v1.full.pdf) by J.S. Inda-Diaz, A. Johnning, M. Hessel, A. Sjo ̈berg, A. Lokrantz, L. Hellda, M. Jirstrand, L. Svensson and E. Kristiansson (Chalmers University of Technology and University of Gothenburg/Centre for Antibiotic Resistance Research (CARe), 2023) 🔥
> 231. [Framework based on conformal predictors and power martingales for detection of fixed football matches](https://www.semanticscholar.org/paper/Framework-based-on-conformal-predictors-and-power-Zhuk-Chertov/9405c6a70f5ce211afa575b4bdc4cddb449e9594?utm_source=alert_email&utm_content=AuthorCitation&utm_campaign=AlertEmails_DAILY&utm_term=LibraryFolder+AuthorCitation&email_index=1-0-3&utm_medium=15942999) by I. Zhuk, O. Chertov (2023)
> 232. [Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems](https://arxiv.org/pdf/2305.10124.pdf) by Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad (2023) 🔥
> 233. [Conformalized matrix completion](https://arxiv.org/pdf/2305.10637.pdf) by Yu Gui, Rina Foygel Barber, and Cong Ma (University of Chicago, 2023) 🔥 [code](https://github.com/yugjerry/conf-mc)
> 234. [Conformal Prediction With Conditional Guarantees](https://arxiv.org/pdf/2305.12616.pdf) by Isaac Gibbs, John Cherian, Emmanuel Candes (Stanford, 2023) [code](https://github.com/jjcherian/conditional-conformal) 🔥
> 235. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://arxiv.org/abs/2305.14535) by Kexin Huang, Ying Jin, Emmanuel Candes, Jure Leskovec (Stanford, 2023) [code](https://github.com/snap-stanford/conformalized-gnn) 🔥
> 236. [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://arxiv.org/pdf/2305.17564.pdf) by Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar (MIT/Berkeley, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) [video](https://www.youtube.com/watch?v=Ess8_S4avW4) 🔥
> 237. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/pdf/2305.18404.pdf) by Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (Harvard/MIT, 2023) 🔥
> 238. [Conformal Predictive Distribution Trees](https://link.springer.com/content/pdf/10.1007/s10472-023-09847-0.pdf) by Ulf Johansson, Tuwe Löfström, Henrik Boström (2023) 🔥
> 239. [CONFORMAL PREDICTION WITH PARTIALLY LABELED DATA](https://arxiv.org/pdf/2306.01191.pdf) by Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier (2023)
> 240. [Conformal Prediction for Federated Uncertainty Quantification Under Label Shift](https://arxiv.org/pdf/2306.05131.pdf) by Vincent Plassie, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov (2023)
> 241. [Conformalizing Machine Translation Evaluation](https://arxiv.org/pdf/2306.06221.pdfm) by Chrysoula Zerva, André F. T. Martins (2023)
> 242. [Class-Conditional Conformal Prediction With Many Classes](https://arxiv.org/pdf/2306.09335.pdf) by Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani 🔥 (Berkeley, 2023)
> 243. [Conformal Prediction Sets for Graph Neural Networks](https://openreview.net/pdf/1915b7fb247001d17114b5908727ac0f7056abe8.pdf) by Soroush Zargarbashi, Simone Antonelli, Aleksandar Bojchevski [Code](https://github.com/bhaweshiitk/ConformalLLM)
> 244. [Conformal link prediction to control the error rate](https://arxiv.org/pdf/2306.14693v1.pdf) by Ariane Marandon (2023)
> 245. [JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift](https://openreview.net/pdf?id=ORxBEWMPAJ) Drew Prinster, Suchi Saria, Anqi Liu (John Hopkins University, 2023) [Code](https://github.com/drewprinster/jaws-x) 🔥
> 246. [Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction](https://arxiv.org/pdf/2306.17815.pdf) by Yunchuan Zhang, Sangwoo Park and Osvaldo Simeone (2023)
> 247. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://arxiv.org/abs/2307.01928) by Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar (Princeton/DeepMind, 2023) [Website](https://robot-help.github.io/) 🔥
> 248. [Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery](https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-018-0325-4.pdf) by Nicolas Bosc, Francis Atkinson, Eloy Felix, Anna Gaulton, Anne Hersey and Andrew R. Leach (Cambridge, 2019) 🔥
> 249. [Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers](https://www.diva-portal.org/smash/get/diva2:1163353/FULLTEXT01.pdf) by Henrik Linusson, Ulf Johansson, Henrik Bostroem, and Tuve Loefstroem (2014)
> 250. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/pdf/2302.03791.pdf) [Code](https://github.com/Sulam-Group/k-rcps) (John Hopkins University, 2023) 🔥
> 251. [Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors](https://mdpi-res.com/d_attachment/applsci/applsci-13-08647/article_deploy/applsci-13-08647.pdf?version=1690443630) by Gang Wang, Zhiying Lu, Ping Wang, Shuo Zhuang and Di Wang (2023)
> 252. [Plug-in martingales for testing exchangeability on-line](https://icml.cc/2012/papers/808.pdf) by Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk (Royal Holloway, UK, ICML 2012) 🔥
> 253. [Testing Exchangeability On-Line](https://aaai.org/papers/100-testing-exchangeability-on-line/) by Vladimir Vovk, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, UK, ICML 2003) 🔥
> 254. [Predictive Inference Is Free with the Jackknife±after-Bootstrap](https://proceedings.neurips.cc/paper/2020/file/2b346a0aa375a07f5a90a344a61416c4-Paper.pdf) by Byol Kim, Chen Xu, Rina Foygel Barber (University of Chicago, 2020) 🔥
> 255. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/abs/2305.18404) by Bhawesh Kumar, Charles Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (MIT, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) 🔥
> 256. [CONFORMAL PREDICTIONS ENHANCED EXPERT-GUIDED MESHING WITH GRAPH NEURAL NETWORKS](https://arxiv.org/pdf/2308.07358.pdf) [code](https://github.com/ahnobari/AutoSurf) by Amin Heyrani Nobari, Justin Rey, Suhas Kodali and Matthew Jones (MIT, 2023) [website](https://decode.mit.edu/projects/autosurf/) 🔥
> 257. [Approximating Full Conformal Prediction at Scale via Influence Functions](https://arxiv.org/pdf/2202.01315.pdf) by Javier Abad, Umang Bhatt, Adrian Weller, Giovanni Cherubin (Cambridge, Alan Turing Institute, ETH, Microsoft Research, 2023) [code](https://github.com/cambridge-mlg/acp) [video](https://www.youtube.com/watch?v=LRwm976poDE) 🔥
> 258. [Robust Uncertainty Quantification using Conformalised Monte Carlo Prediction](https://arxiv.org/pdf/2308.09647.pdf) [Code](https://github.com/team-daniel/MC-CP) by Daniel Bethell, Simos Gerasimou, Radu Calinescu (University of York, 2023) 🔥
> 259. [I do not know! but why?”– Local Model-Agnostic Example-based explanations of reject](https://www.sciencedirect.com/science/article/pii/S0925231223008457) [code](https://github.com/HammerLabML/LocalModelAgnosticExamplebasedExplanationsReject) by Andre Artelt, Roel Visser and Barbara Hammer (University of Bielefeld, 2023)
> 260. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://arxiv.org/pdf/2308.11975.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir and Ulf Johansson (KTH/Joenkoeping University, 2023) 🔥
> 261. [Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions](https://arxiv.org/pdf/2212.13629.pdf) by Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel (Princeton University, Columbia University, Harvard University, University of Toronto, 2023) 🔥
> 262. [Improving Deep Learning-Based Defect Classification in Solar Cells using Conformal Prediction](https://backend.orbit.dtu.dk/ws/portalfiles/portal/333872349/FinalManuscript_2022_714_0602131208.pdf) by Vitus Bødker Thomsen, Claire Mantel, Gisele Benatto, Søren Forchhammer (DTU - Technical University of Denmark, 2023) 🔥
> 263. [Adaptive Conformal Prediction by Reweighting Nonconformity Scores](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou and Nicolas J-B. Brunel and Nicolas J-B. Brunel (University Paris Saclay, Stellantis Paris, Quantmetry Paris, 2023) [code](https://github.com/salimamoukou/ACPI)
> 264. [Copula-based conformal prediction for Multi-Target Regression](https://arxiv.org/abs/2101.12002) by Soundouss Messoudi, Sébastien Destercke, Sylvain Rousseau (2021) [code](https://github.com/M-Soundouss/CopulaConformalMTR) 🔥
> 265. [Conformal Meta-learners for Predictive Inference of Individual Treatment Effects](https://arxiv.org/abs/2308.14895) by Ahmed Alaa, Zaid Ahmad, and Mark van der Laan (2023) [code](https://github.com/AlaaLab/conformal-metalearners) 🔥
> 266. [Adaptive conformal classification with noisy labels](https://arxiv.org/pdf/2309.05092.pdf) by Matteo Sesia, Y. X. Rachel Wang†, Xin Tong [code](https://github.com/msesia/conformal-label-noise) 🔥
> 267. [Heteroskedastic conformal regression](https://arxiv.org/pdf/2309.08313.pdf) by NICOLAS DEWOLF, BERNARD DE BAETS AND WILLEM WAEGEMAN (2023) 🔥 (UGent, 2023)
> 268. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://arxiv.org/pdf/2309.08603.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Stanford, 2023) 🔥
> 269. [Conformal Temporal Logic Planning using Large Language Models: Knowing When to Do What and When to Ask for Help](https://arxiv.org/abs/2309.10092) [project website](https://ltl-llm.github.io/) by Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros (Washington University in St.Louis and University of Zurich, 2023) 🔥
> 270. [Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method](https://www.nature.com/articles/s41598-022-17609-x) [code](https://github.com/unisb-bioinf/Conformal-Drug-Sensitivity-Prediction) by Kerstin Lenhof, Lea Eckhart, Nico Gerstner, Tim Kehl & Hans-Peter Lenhof (Saarland University, 2022) 🔥
> 271. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candès (Stanford, 2019) [code](Conformalized Quantile Regression]([https://github.com/yromano/cqr](https://github.com/yromano/cqr)) [project](https://sites.google.com/view/cqr) 🔥
> 272. [Fast nonlinear vector quantile regression](https://arxiv.org/pdf/2205.14977.pdf) by Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano and Alex M. Bronstein (Technion, 2023) [code](https://github.com/vistalab-technion/vqr) 🔥
> 273. [Testing for Outliers with Conformal p-values](https://arxiv.org/abs/2104.08279) by Stephen Bates, Emmanuel Candes,Lihua Lei, Yaniv Romano,Matteo Sesia (Berkeley/Stanford/Technion, 2022) [code](https://github.com/msesia/conditional-conformal-pvalues) 🔥
> 274. [Achieving Risk Control in Online Learning Settings](https://arxiv.org/abs/2205.09095) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (Technion/Berkeley, 2021) [code](https://github.com/Shai128/rrc)
> 275. [Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ell2.12860) by Lorin Werthen-Brabants, Tom Dhaene, Dirk Deschrijver (Ghent University, 2023) 🔥
> 276. [Online NoVaS Conformal Volatility Prediction](https://proceedings.mlr.press/v204/canete23b/canete23b.pdf) [slides](https://copa-conference.com/presentations/3%20-%20COPA-2023.NoVas.v005.pptx) by Alejandro Canete (University of Chicago) 🔥 (2023)
> 277. [Market Implied Conformal Volatility Intervals](https://proceedings.mlr.press/v204/canete23a/canete23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023.ImpliedIntervals.v005.pptx) by Alejandro Canete (University of Chicago) 🔥 (2023)
> 278. [PUNCC: a Python Library for Predictive Uncertainty Calibration and Conformalization](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) [code](https://github.com/deel-ai/puncc) 🔥
> 279. [The Venn-ABERS Testing for Change-Point Detection](https://proceedings.mlr.press/v204/nouretdinov23b/nouretdinov23b.pdf) [slides](https://copa-conference.com/presentations/Nouretdinov_poster_slides.pdf) by Ilia Nouretdinov and Alex Gammerman (Royal Holloway, 2023) 🔥
> 280. [A Review of Nonconformity Measures for Conformal Prediction in Regression](https://proceedings.mlr.press/v204/kato23a/kato23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023%20-%20v2.pptx) by Yuko Kato, David M.J. Tax, Marco Loog (Delft University of Technology, Radboud University, Nijmegen, Netherlands, 2023)
> 281. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://proceedings.mlr.press/v204/alkhatib23a/alkhatib23a.pdf) [slides](https://copa-conference.com/presentations/Amr-Alkhatib.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir, Ulf Johansson (KTH, Joenkoeping University, 2023).
> 282. [Mondrian Predictive Systems for Censored Data](https://proceedings.mlr.press/v204/bostrom23a/bostrom23a.pdf) [slides](https://copa-conference.com/presentations/Bostrom.pdf) by Henrik Bostroem, Henrik Linusson, Anders Vesterberg (KTH, Ekkono Solutions AB, Scania CV AB, Sweden 2023).
> 283. [Evaluating Machine Translation Quality with Conformal Predictive Distributions](https://proceedings.mlr.press/v204/giovannotti23a/giovannotti23a.pdf) [slides](https://copa-conference.com/presentations/MTpres.pdf) by Patrizio Giovannotti (Royal Holloway, UK) 🔥
> 284. [Confidence Calibration for Systems with Cascaded Predictive Modules](https://arxiv.org/pdf/2309.12510.pdf) by Yunye Gong, Yi Yao, Xiao Lin, Ajay Divakaran, Melinda Gervasio (SRI International, 2023)
> 285. [TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images](https://arxiv.org/pdf/2307.15638.pdf) by Benjamin Lambert, Florence Forbes, Senan Doyle, and Michel Dojat (Grenoble Institut Neurosciences / Univ. Grenoble Alpes, Pixyl, 2023) 🔥
> 286. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES 🚀🔥
> 287. [Distribution-free risk assessment of regression-based machine learning algorithms](https://arxiv.org/pdf/2310.03545.pdf) by Sukrita Singh, Neeraj Sarna, Yuanyuan Li, Yang Lin, Agni Orfanoudaki (University of Oxford / MunichRe, 2023).
> 288. [CONFORMAL PREDICTION FOR DEEP CLASSIFIER VIA LABEL RANKING](https://arxiv.org/pdf/2310.06430.pdf) by Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei (Southern University of Science and Technology, ShanghaiTech University, Rutgers University, University of North Carolina at Chapel Hill, Chongqing University) 🔥 [code](https://github.com/ml-stat-Sustech/conformal_prediction_via_label_ranking)
> 289. [On the Expected Size of Conformal Prediction Sets](https://arxiv.org/abs/2306.07254) by Guneet S. Dhillon, George Deligiannidis, Tom Rainforth (University of Oxford, 2023) [code](https://github.com/Guneet-Dhillon/expected-conformal-prediction-set-size)🔥
> 290. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)🔥
> 291. [Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification](https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Melki_Group-Conditional_Conformal_Prediction_via_Quantile_Regression_Calibration_for_Crop_and_ICCVW_2023_paper.html) by Paul Melki, Lionel Bombrun, Boubacar Diallo, Jérôme Dias, Jean-Pierre Da Costa by EXXACT Robotics (2023) 🔥
> 292. [Guaranteed Coverage Prediction Intervals with Gaussian Process Regression](https://arxiv.org/abs/2310.15641) by Harris Papadopoulos (2023) 🔥
> 293. [Conformal prediction with missing values](https://arxiv.org/abs/2306.02732) by Margaux Zaffran, Aymeric Dieuleveut, Julie Joss, Yaniv Romano (2023) [Video](https://icml.cc/virtual/2023/poster/23530) [summary](https://mzaffran.github.io/assets/files/Posters/cp_na_summary.pdf) [code](https://github.com/mzaffran/ConformalPredictionMissingValues) 🔥
> 294. [TRIAGE: Characterizing and auditing training data for improved regression](https://arxiv.org/pdf/2310.18970.pdf) by Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar (University of Cambridge, 2023) [code](https://github.com/seedatnabeel/TRIAGE)) 🔥
> 295. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series) TIME SERIES 🚀🔥
> 296. [Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model](https://openreview.net/pdf?id=VeO03T59Sh) by Jiankai Sun, Jiankai\_Sun, Yiqi Jiang, Jianing Qiu, Parth Talpur Nobel, Mykel Kochenderfer, Mac Schwager (Staford, Imperial College, 2023)
> 297. [CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift](https://openreview.net/pdf?id=GgdFLb94Ld) by Siddhartha Laghuvarapu, Zhen Lin, Jimeng Sun (University of Illinois Urbana-Champaign, 2023)
> 298. [PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction](https://openreview.net/forum?id=URrUpcp6Qh) Apoorva Sharma, Sushant Veer, Asher Hancock, Heng Yang, Marco Pavone, Anirudha Majumdar (NVIDIA Research, Harvard, Princeton, Stanford 2023) 🔥
> 299. [Conformal Prediction Sets for Ordinal Classification](https://openreview.net/pdf?id=YI4bn6aAmz) by Prasenjit Dey, Srujana Merugu, Sivaramakrishnan Kaveri (Amazon, 2023) 🔥
> 300. [Beyond Confidence: Reliable Models Should Also Consider Atypicality](https://openreview.net/pdf?id=xDCmlkSavR) by Mert Yuksekgonul, Linjun Zhang, James Zou, Carlos Guestrin (Stanford/Rutgers) [code](https://github.com/mertyg/beyond-confidence-atypicality) 🔥
> 301. [On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning](https://arxiv.org/abs/2311.12688) by Paul Scemama, Ariel Kapusta (The Mitre Corporation, 2023)
> 302. [A powerful rank-based correction to multiple testing under positive dependency](https://arxiv.org/pdf/2311.10900.pdf) by Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick (Bosch AI, 2023)
> 303. [Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models](https://arxiv.org/pdf/2311.13628.pdf) by Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel (Columbia University, 2023) 🔥
> 304. [Risk-Aware and Explainable Framework for Ensuring Guaranteed Coverage in Evolving Hardware Trojan Detection](https://arxiv.org/abs/2312.00009) by Rahul Vishwakarma, Amin Rezaei (California State University, 2023) 🔥
> 305. [Uncertainty quantification in automated valuation models with locally weighted conformal prediction](https://arxiv.org/abs/2312.06531) by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams (University of Oslo, North Carolina State University, 2023) 🔥
> 306. [Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates](https://arxiv.org/abs/2312.07434) by Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann [code](https://github.com/nandantumu/conformal_region_designer) (University of Pennsylvania, 2023) 🔥
> 307. [Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction](https://arxiv.org/abs/2312.08604) by Albert Lin, Somil Bansal (University of Southern California, 2023) 🔥
> 308. [Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms](https://arxiv.org/abs/2311.11837) [code](https://github.com/NKI-AI/kandinsky-calibration) by Joren Brunekreef, Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen (The Netherlands Cancer Institute, University of Amsterdam) (2023)
> 309. [Forecasting CPI inflation under economic policy and geo-political uncertainties](https://arxiv.org/abs/2401.00249) by Shovon Sengupta, Tanujit Chakraborty, Sunny Kumar Singh (Fidelity Investments, Sorbonne University, BITS Pilani Hyderabad). (2023) 🔥
> 310. [Conformal prediction of option prices](https://www.repository.utl.pt/handle/10400.5/29690) by Bastas Joao (University of Lisbon, 2023) 🔥
> 311. [Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction](https://arxiv.org/abs/2401.01549) by Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, Mengdi Huai (Iowa University, Pennsylvania State University, Georgia Institute of Technology (2024).
> 312. [Greek Proverbs: Computational Spatial Attribution](https://www.researchsquare.com/article/rs-3360387/v2) by John Pavlopoulos and Panos Louridas, Athens University of Economics and Business, Greece (2024)
> 313. [Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations](https://arxiv.org/pdf/2401.01977.pdf) by Bingkai Wang, Fan Li and Mengxin (University of Michigan, Yale, University of Pennsylvania)
> 314. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES 🚀🔥
> 
> ## Papers Time Series
> 
> 1. [Conformal prediction interval for dynamic time-series](https://proceedings.mlr.press/v139/xu21h.html) by Chen Xu, Yao Xie (Georgia Tech, 2021) TIME SERIES 🚀🔥 [Python Code](https://github.com/hamrel-cxu/EnbPI) [Video](https://vimeo.com/583595474) [Video ICML2021](https://papertalk.org/papertalks/32044)
> 2. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) [Python Code](https://github.com/hamrel-cxu/Ensemble-Regularized-Adaptive-Prediction-Set-ERAPS) TIME SERIES 🚀🔥
> 3. [Conformal Time-Series Forecasting](https://proceedings.neurips.cc/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf) by Kamile Stankeviciu te and Ahmed M. Alaa (2021) [Video NeurIPS2021](https://papertalk.org/papertalks/36577) [Video](https://www.youtube.com/watch?v=xmPDk4QRAAI) TIME SERIES 🚀🔥
> 4. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2202.08756.pdf) by Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen (2022). TIME SERIES 🚀🔥
> 5. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) [Python Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries) TIME SERIES 🚀🔥 [Video](https://www.youtube.com/watch?v=Yuxu9aUpVi0)
> 6. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://arxiv.org/pdf/2205.09095.pdf) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🔥 [Code](https://github.com/Shai128/rci)
> 7. [Conformal Prediction with Temporal Quantile Adjustments](https://arxiv.org/pdf/2205.09940.pdf) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (2022) TIME SERIES 🚀🔥
> 8. [Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data](http://proceedings.mlr.press/v75/chernozhukov18a/chernozhukov18a.pdf) by Victor Chernozhukov (MIT), Kaspar Wuethrich (University of California, San Diego) and Yinchu Zhu (University of Oregon) (2018) [Video](https://www.youtube.com/watch?v=Wcm9Uw0YL8A&t=9s)
> 9. [Distributional Conformal Prediction](https://www.pnas.org/doi/pdf/10.1073/pnas.2107794118) by Chernozhukov (MIT), Kaspar Wuethrich (University of California, San Diego) and Yinchu Zhu (University of Oregon) (2022)
> 10. [Distribution-Free Prediction Bands for Multivariate Functional Time Series: an Application to the Italian Gas Market](https://arxiv.org/abs/2107.00527) by Jacopo Diquigiovanni (University of Padua) Matteo Fontana (Joint Research Centre - European Commission) Simone Vantini (Politecnico di Milano) (2021)
> 11. [CODiT: Conformal Out-of-Distribution Detection in Time- Series Data](https://arxiv.org/pdf/2207.11769.pdf) by Ramneet Kaur et.al., Unibersity of Pensylvania (2022). [Code](https://github.com/kaustubhsridhar/time-series-OOD) TIME SERIES 🚀🔥🔥
> 12. [A General Framework For Multi-step Ahead Adaptive Conformal Heteroscedastic Time Series Forecasting](https://arxiv.org/pdf/2207.14219.pdf) by Martim Sousa, Ana Maria Tomé, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES 🚀🔥
> 13. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/pdf/1905.07886.pdf) by Christopher Kath and Florian Ziel (2020)
> 14. [Robust Gas Demand Forecasting With Conformal Prediction](https://copa-conference.com/papers/COPA2022_paper_12.pdf) by Mouhcine Mendil, Luca Mossina, Marc Nabhan, Kevin Pasini (2022)
> 15. [Conformal Prediction Bands for Two-Dimensional Functional Time Series](https://arxiv.org/pdf/2207.13656.pdf) by Niccolo` Ajroldia, Jacopo Diquigiovannib, Matteo Fontanac, Simone Vantinia (2022)
> 16. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🔥
> 17. [Conformal prediction set for time-series](Conformal prediction set for time-series) by Chen Xu, Yao Xie (Georgia Tech, 2022) [Code](https://github.com/hamrel-cxu/Ensemble-Regularized-Adaptive-Prediction-Set-ERAPS)
> 18. [Amazon Fortuna](https://aws-fortuna.readthedocs.io/en/latest/examples/enbpi_ts_regression.html)
> 19. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) [code](https://github.com/salesforce/online_conformal) TIME SERIES 🚀🔥
> 20. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://arxiv.org/pdf/2303.12783.pdf) by Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter (Johannes Kepler University, Linz, 2023) code [blogpost](https://ml-jku.github.io/HopCPT/) TIME SERIES 🚀🔥
> 21. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/abs/2212.03463) by Chen Xu, Yao Xie (Georgia Tech) [code](https://github.com/hamrel-cxu/SPCI-code/tree/main) TIME SERIES 🚀🔥
> 22. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES 🚀🔥
> 23. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series) TIME SERIES 🚀🔥
> 24. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES 🚀🔥
> 
> ## Presentation Slides
> 
> 1. [Machine Learning for Probabilistic Prediction, Seattle Artificial Intelligence Workshops Meetup](https://www.researchgate.net/publication/371566526_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin, 2023 🔥
> 2. [Machine Learning for Probabilistic Prediction](https://github.com/valeman/awesome-conformal-prediction/blob/main/Machine%20Learning%20for%20Probabilistic%20Prediction.pdf) by Valery Manokhin, 2022 🔥
> 3. [Adaptive Conformal Anomaly Detection for Time-series](https://cml.rhul.ac.uk/copa2017/presentations/burnaev.pdf) by Evgeny Burnaev, Alexander Bernstein, Vlad Ishimtsev and Ivan Nazarov (Skoltech, Moscow, Russia, 2017)
> 4. [Nonparametric predictive distributions based on conformal prediction](https://cml.rhul.ac.uk/copa2017/presentations/vovk.pdf) by Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, University of London Rutgers University, 2017)
> 5. [What Can Conformal Inference Offer to Statistics?](https://lihualei71.github.io/Job_Talk_Lihua_Lei.pdf) by Lihua Lei, Stanford University
> 6. [Conformal Regressors and Predictive Systems – a Gentle Introduction](https://copa-conference.com/presentations/COPA_2022_Presentation __Conformal_Regressors_and_Predictive_Systems___ a_Gentle_Introduction.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
> 7. [Applications of Conformal Predictors](https://copa-conference.com/presentations/lars_ernst.pdf) by Ernst Ahlberg and Lars Carlsson (Stena Line, 2022)
> 8. [crepes: a Python Package for Conformal Regressors and Predictive Systems](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
> 9. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/presentations/amr.pdf) by Amr Alkhatib, Henrik Boström and Ulf Johansson (2022)
> 10. [Well-Calibrated Rule Extractors](https://copa-conference.com/presentations/Johansson_U_COPA2022_pres.pdf) by Ulf Johansson, Tuwe Löfström, Niclas Ståhl (2022)
> 11. [Calibration of Natural Language Understanding Models with Venn-ABERS Predictors](Calibration of Natural Language Understanding Models with Venn-ABERS Predictors]([https://copa-conference.com/presentations/patrizio.pdf](https://copa-conference.com/presentations/patrizio.pdf)) by Patrizio Giovannotti (2022)
> 12. [Reinforcement Learning Prediction Intervals with Guaranteed Fidelity](https://web.engr.oregonstate.edu/~tgd/talks/dietterich-caml-rl-prediction-intervals.pdf) by Thomas Dietterich (University of Oregon, 2022)
> 13. [Conformal Prediction beyond exchangeability](https://copa-conference.com/presentations/conformal_UAI_tutorial_2.pdf) by Rina Foygel Barber (University of Chicago, 2022) 🔥
> 14. [Split conformal prediction for dependant data](http://www.im.ufrj.br/~coloquiomea/apresentacoes/orenstein_2022.pdf) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos and João Vitor Romano (2022)
> 15. [Conformal prediction of small-molecule drug resistance in cancer cell lines](https://copa-conference.com/presentations/saiveth.pdf) by Saiveth Hernandez-Hernandez, Sachin Vishwakarma and Pedro Ballester
> 16. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/pdf/2212.03463.pdf) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES 🔥
> 17. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://arxiv.org/abs/2212.03281) by Sophia Sun, Rose Yu (University of California, San Diego, 2022)
> 18. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, 2022)
> 19. [Conformal Prediction: an Introduction](https://github.com/online-ml/river/files/10098389/Conformal_Prediction_Presentation.pdf) by Leo Andeol (2022)
> 20. [Adaptive Conformal Predictions for Time Series](https://github.com/valeman/awesome-conformal-prediction/blob/main/Adaptive%20Conformal%20Predictions%20for%20Time%20Series.pdf) by Margaux Zaffran (ICML,2021)
> 21. [EnbPI poster](https://github.com/valeman/awesome-conformal-prediction/blob/main/EnbPI_poster.pdf) by Chen Xu, Yao Xie (2021)
> 22. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/370012303_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin (2023) 🔥
> 23. [Conformal Inference with Tidymodels](https://topepo.github.io/posit-2023-conformal/#/title-slide) by Max Kuhn (posit conference 2023) 🔥
> 24. [Graceful handling of large imbalanced datasets using Conformal Prediction](https://bigchem.eu/sites/default/files/AZ_school_Norinder_public.pdf) by Ulf Norinder and Fredrik Svensson 🔥
> 25. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candès (Stanford, 2019)
> 26. [Leveraging Conformal Prediction for Calibrated Probabilistic Time Series Forecasts to Accelerate the Renewable Energy Transition](https://speakerdeck.com/ingevandenende/leveraging-conformal-prediction-for-calibrated-probabilistic-time-series-forecasts-to-accelerate-the-renewable-energy-transition) by Inge van den Ende (Dexter Energy, 2023). TIME SERIES 🚀🔥
> 
> ## Researchers
> 
> 1. [Vladimir Vovk](https://github.com/valeman/awesome-conformal-prediction/blob/main/vovk.net), Royal Holloway, United Kingdom
> 2. [Alexander Gammerman](https://cml.rhul.ac.uk/people/alex/), Royal Holloway, United Kingdom
> 3. [Glenn Shafer](http://www.glennshafer.com/), Rutgers University, USA
> 4. [Emmanuel Candès](https://candes.su.domains/), Stanford, USA
> 5. [Ryan Tibshiriani](https://www.stat.cmu.edu/~ryantibs/), Carnegie Mellon, USA
> 6. [Yaniv Romano](https://sites.google.com/view/yaniv-romano/), Technion—Israel Institute of Technology
> 7. [Michael I. Jordan](https://people.eecs.berkeley.edu/~jordan/), Berkeley, USA
> 8. [Jitendra Malik](https://people.eecs.berkeley.edu/~malik/), Berkeley, USA
> 9. [Anastasios Angelopoulos](https://people.eecs.berkeley.edu/~angelopoulos/), Berkeley, USA
> 10. [Lihua Lei](https://lihualei71.github.io/), Stanford, USA
> 11. [Henrik Boström](https://www.kth.se/profile/henbos), KTH, Sweden
> 12. [Ulf Johansson](https://scholar.google.com/citations?user=OZjCgIsAAAAJ&hl=en), Jönköping University, Sweden
> 13. [Henrik Linusson](https://scholar.google.se/citations?user=Xl8W39cAAAAJ&hl=en), University of Borås, Sweden
> 14. [Harris Papadopoulos](http://staff.fit.ac.cy/com.ph/), Frederick University, Cyprus
> 15. [Vladimir V’yugin](http://iitp.ru/ru/users/125.htm), Institute for Information Transmission Problems (IITP), Russia
> 16. [Evgeny Burnaev](https://faculty.skoltech.ru/people/evgenyburnaev), Skoltech, Russia
> 17. [Aaditya Ramdas](http://stat.cmu.edu/~aramdas/), Carnegie Mellon, USA
> 18. [Benjamin LeRoy](https://benjaminleroy.github.io/), Carnegie Mellon, USA
> 19. [Victor Chernozhukov](https://www.mit.edu/~vchern/), MIT, USA
> 20. [Ulf Norinder](https://scholar.google.com/citations?user=i5hUEFwAAAAJ&hl=en), Stockholm University, Sweden
> 21. [Ola Spjuth](https://pharmb.io/), Uppsala University, Sweden
> 22. [Ilia Nouretdinov](https://cml.rhul.ac.uk/people/nouretdinov/index.htm), Royal Holloway, United Kingdom
> 23. [Matteo Fontana](https://scholar.google.com/citations?user=U7jODH8AAAAJ&hl=it), Royal Holloway, University of London, United Kingdom
> 24. [Yao Xie](https://www2.isye.gatech.edu/~yxie77/), Georgia Institute of Technology
> 25. [Zhimeo Ren](https://zhimeir.github.io/), University of Chicago
> 26. [Rafael Izbicki](http://www.rizbicki.ufscar.br/), Federal University of São Carlos (UFSCar) Brazil
> 27. [Rina Foygel Barber](https://rinafb.github.io/) University of Chicago
> 28. [Matteo Sesia](https://msesia.github.io/) University of Southern California, Marshall School of Business
> 29. [Simone Vantini](https://scholar.google.it/citations?user=Cy2mv3YAAAAJ&hl=it) MOX - Department of Mathematics, Politecnico di Milano
> 
> ## Articles
> 
> 1. [Measuring Models’ Uncertainty: Conformal Prediction](https://blog.dataiku.com/measuring-models-uncertainty-conformal-prediction) by Leo Dreyfus-Schmidt (Dataiku, 2020). 🔥
> 2. [Conformal Prediction for Neural Regression Model](https://pkghosh.wordpress.com/2021/12/30/conformal-prediction-for-a-neural-regression-model/) by Pranab Ghosh (2021). 🔥
> 3. [How to Handle Uncertainty in Forecasts](https://towardsdatascience.com/how-to-handle-uncertainty-in-forecasts-86817f21bb54) by Michael Berk (2021)
> 4. [How to Add Uncertainty Estimation to your Models with Conformal Prediction](https://towardsdatascience.com/how-to-add-uncertainty-estimation-to-your-models-with-conformal-prediction-a5acdb86ea05) by Zachary Warnes (2021)
> 5. [nonconformist: An easy way to estimate prediction intervals](https://medium.com/spikelab/nonconformist-an-easy-way-to-estimate-prediction-intervals-b0ded1eb066f) by Maria Jesus Ugarte (2021).
> 6. [Detecting Weird Data: Conformal Anomaly Detection](https://towardsdatascience.com/detecting-weird-data-conformal-anomaly-detection-20afb36c7bcd) by Matthew Burruss (2020).
> 7. [“MAPIE” Explained Exactly How You Wished Someone Explained to You](https://towardsdatascience.com/mapie-explained-exactly-how-you-wished-someone-explained-to-you-78fb8ce81ff3) by Samuele Mazzanti (2022). 🔥
> 8. [With MAPIE, uncertainties are back in machine learning](https://towardsdatascience.com/with-mapie-uncertainties-are-back-in-machine-learning-882d5c17fdc3) by Vianney Taquet (2021) 🔥
> 9. [How to Predict Risk-Proportional Intervals with Conformal Quantile Regression](https://towardsdatascience.com/how-to-predict-risk-proportional-intervals-with-conformal-quantile-regression-175775840dc4) by Samuele Mazzanti (2022). 🔥
> 10. [Stanford statisticians and Washington Post data scientists build more honest prediction models](https://news.stanford.edu/2021/03/19/honesty-statistical-models/#) Stanford (2021) 🔥
> 11. [How to Detect Anomalies — state-of-the-art methods using Conformal Prediction](https://valeman.medium.com/how-to-detect-anomalies-state-of-the-art-methods-using-conformal-prediction-e02691659412) by Valery Manokhin (2021) 🔥
> 12. [How to calibrate your classifier in an intelligent way](https://medium.com/@valeman/how-to-calibrate-your-classifier-in-an-intelligent-way-a996a2faf718) by Valery Manokhin (2022) 🔥
> 13. [Conformal Prediction forecasting with MAPIE](https://medium.com/@valeman/conformal-prediction-forecasting-with-mapie-library-for-conformal-prediction-7aac033ae3ef) by Valery Manokhin (2022) TIME SERIES 🚀🔥
> 14. [How to predict full probability distribution using machine learning Conformal Predictive Distributions](https://medium.com/@valeman/how-to-predict-full-probability-distribution-using-machine-learning-conformal-predictive-f8f4d805e420) by Valery Manokhin (2022) 🔥
> 15. [How to predict quantiles in a more intelligent way (or ‘Bye-bye quantile regression, hello Conformal Quantile Regression](https://valeman.medium.com/how-to-predict-quantiles-in-a-more-intelligent-way-or-bye-bye-quantile-regression-hello-24a65e4c50f) by Valery Manokhin (2022) 🔥
> 16. [Conformal Prediction in Julia, Part I - Introduction](https://towardsdatascience.com/conformal-prediction-in-julia-351b81309e30) by Patrick Altmeyer (2022)
> 17. [Getting predictions intervals with conformal inference](http://projects.rajivshah.com/blog/2022/09/24/conformal_predictions/) by Rajiv Shah (2022)
> 18. [How to Conformalize a Deep Image Classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) by Patrick Altmeyer (2022)
> 19. [Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need](https://towardsdatascience.com/time-series-forecasting-with-conformal-prediction-intervals-scikit-learn-is-all-you-need-4b68143a027a) by Marco Cerliani (2022)
> 20. [Conformal Prediction in Julia, Part II - How to conformalize a deep image classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) by Patrick Altmeyer (2022)
> 21. [Conformal Prediction in Julia, Part III - Prediction intervals for any regression model](https://towardsdatascience.com/prediction-intervals-for-any-regression-model-306930d5ad9a) by Patrick Altmeyer (2022)
> 22. [Probabilistic Forecasting with Conformal Prediction and NeuralProphet](https://medium.com/@valeman/probabilistic-forecasting-with-conformal-prediction-and-neuralprophet-af9c87901d94) by Valery Manokhin (2022) TIME SERIES 🚀🔥
> 23. [Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need](https://towardsdatascience.com/time-series-forecasting-with-conformal-prediction-intervals-scikit-learn-is-all-you-need-4b68143a027a) by Marco Cerliani (2022) TIME SERIES 🚀🔥
> 24. [TQA: Creating Valid Prediction Intervals for Cross-sectional Time Series Regression](https://realsunlab.medium.com/tqa-creating-valid-prediction-intervals-for-cross-sectional-time-series-regression-bd0f2260fae7) by Zhen Lin (UIUC, NeurIPS’22 paper) TIME SERIES 🚀🔥
> 25. [Conformal Prediction: A Critic to Predictive Models](https://medium.com/@data-overload/conformal-prediction-a-critic-to-predictive-models-27501dcc76d4) (2023)
> 26. [Multi-horizon Probabilistic Forecasting with Conformal Prediction and NeuralProphet](https://valeman.medium.com/multi-horizon-probabilistic-forecasting-with-conformal-prediction-and-neuralprophet-5ec5af3888c8) by Valery Manokhin TIME SERIES 🚀🔥
> 27. [Putting clear bounds on uncertainty](https://news.mit.edu/2023/putting-clear-bounds-uncertainty-0123) (MIT, 2023)
> 28. [Conformal prediction theory explained](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-theory-explained-14a35226df80) by Artem Ryasik (2023)
> 29. [Easy Distribution-Free Conformal Intervals for Time Series](https://towardsdatascience.com/easy-distribution-free-conformal-intervals-for-time-series-665137e4d907) by Michael Keith (2023)
> 30. [Another (Conformal) Way to Predict Probability Distributions](https://towardsdatascience.com/another-conformal-way-to-predict-probability-distributions-fcc63e78680d) by Harrison Hoffman (2023) 🔥
> 31. [How to use full (transductive) Conformal Prediction])([https://valeman.medium.com/how-to-use-full-transductive-conformal-prediction-7ed54dc6b72b](https://valeman.medium.com/how-to-use-full-transductive-conformal-prediction-7ed54dc6b72b)) by Valery Manokhin (2023) 🔥
> 32. [Conformal Prediction for Regression](https://medium.com/@artem_ryasik/conformal-prediction-for-regression-1ba24f1442df) (using KNIME) by Artem Ryasik (2023)
> 33. [Dynamic Conformal Intervals for any Time Series Model](https://towardsdatascience.com/dynamic-conformal-intervals-for-any-time-series-model-d1638aa48527) by Michael Keith (2023) TIME SERIES 🚀🔥
> 34. [Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting](https://towardsdatascience.com/hitting-time-forecasting-the-other-way-for-time-series-probabilistic-forecasting-6c3b6496c353) by Marco Cerliani (2023) TIME SERIES 🚀🔥🔥🔥🔥
> 35. [Series of Medium articles about Conformal Prediciton (in Portuguese)](https://github.com/gusbruschi13/Conformal-Prediction) by Gustavo Bruschi (2023) 🔥
> 36. [Stanford statisticians and Washington Post data scientists build more honest prediction models](https://news.stanford.edu/2021/03/19/honesty-statistical-models/) [Code](https://github.com/washingtonpost/elex-live-model) 🔥
> 37. [Jackknife+ — a Swiss knife of Conformal Prediction for regression](https://medium.com/@valeman/jackknife-a-swiss-knife-of-conformal-prediction-for-regression-ce3b56432f4f) by Valeriy Manokhin (2023) 🔥
> 38. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)🔥
> 39. [How to use Conformal Prediction](https://kiel.ai/conformal-prediction/) by Yannick Kälber (2023) 🔥
> 40. [Model Diagnostics: Prediction Uncertainty](https://piml.medium.com/model-diagnostics-prediction-uncertainty-990ff0bd2c4e) by PiML team, Wells Fargo (2023).
> 41. [Make any predictor uncertainty-aware via conformal prediction](https://mlwithouttears.com/2023/11/15/make-any-predictor-uncertainty-aware-via-conformal-prediction/) by Lorenzo Maggi (2023)
> 42. [Conformal predictive systems - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-predictive-systems-895c40bac2ca) by Artem Ryasik (2023) 🔥
> 43. [Conformal prediction for classification - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-for-classification-6bf152abb491) by Artem Ryasik (2023) 🔥
> 
> ## Kaggle
> 
> 1. [Kaggle Notebook showcasing Conformal Predictive Distributions on Playground Series Season 3, Episode 1 (California Housing data) competition](https://www.kaggle.com/code/predaddict/conformal-predictive-distributions-pss3-e1) by Valeriy Manokhin (2022)
> 2. [Kaggle Notebook showcasing Venn-ABERs Conformal Prediction on Playground Series Season 3, Episode 2 (Stroke prediction) competition](https://www.kaggle.com/predaddict/pss-3-episode-2-jan-2023-stroke-prediction/)
> 3. [Regression prediction intervals with MAPIE](https://www.kaggle.com/code/predaddict/regression-prediction-intervals-with-mapie/edit) by Carl McBride Ellis (2022)
> 
> ## Websites
> 
> 1. [Main website with research from Prof. Vladimir (Volodya) Vovk](http://alrw.net/) 🔥
> 2. [Conformal Prediction - Prediction with guaranteed performance](https://cml.rhul.ac.uk/cp.html) Royal Holloway, United Kingdom
> 3. [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://people.eecs.berkeley.edu/~angelopoulos/blog/posts/gentle-intro/) by Anastasios N. Angelopoulos 🔥
> 4. [Reliable Predictive Inference](https://sites.google.com/view/cqr) by Yaniv Romano 🔥
> 
> ## Twitter
> 
> 1. [Title: What Can _Conformal Inference_ Offer to Statistics?](https://t.co/znZHcyyknV) by Lihua Lei, Stanford, 2022
> 2. [Conformalized survival analysis](https://twitter.com/lihua_lei_stat/status/1381418936739098630?s=20&t=PnfV8wnLV2bThXcKdFFbRg) by Lihua Lei, Stanford, 2021
> 3. [Conformal Risk Control](https://threadreaderapp.com/thread/1555616778578829312.html) by Anastasious Angelopolous, Berkeley, 2022
> 4. [Stable Conformal Prediction Sets](https://threadreaderapp.com/thread/1549413164835536897.html) by Eugene Ndiaye (Georgia Tech, 2022)
> 5. [Machine learning sucks at uncertainty quantification. But there is a solution that almost sounds too good to be true: conformal prediction](https://threadreaderapp.com/thread/1572526084981129217.html) by Cristoph Molnar (2022).
> 6. [How to correctly, yet efficiently model the uncertainty on predictions](https://threadreaderapp.com/thread/1584905067718647808.html) by Nico Wolf (2022)
> 7. [Top 10 Github libraries for Conformal Prediction](https://threadreaderapp.com/thread/1609112769017552897.html) by Valeriy Manokhin (2022)
> 8. [Robots That Ask For Help](https://threadreaderapp.com/thread/1677000811803443213.html) by Allen Z. Ren (2023) 🍾🔥
> 
> ## TikTok
> 
> 1. [Getting prediction intervals with conformal prediction](https://www.tiktok.com/@rajistics/video/7145960334270975275?is_from_webapp=v1&item_id=7145960334270975275) by Rajiv Shah (Hugging Face,2022)
> 2. [Why you want prediction intervals instead of point predictions](https://www.tiktok.com/@rajistics/video/7145295854927236398?is_from_webapp=v1&item_id=7145295854927236398) by Rajiv Shah (Hugging Face,2022)
> 3. [It’s important to make sure your model is well calibrated](https://www.tiktok.com/@rajistics/video/7164607746396933419?is_copy_url=1&is_from_webapp=v1&item_id=7164607746396933419) by Rajiv Shah (Hugging Face,2022)
> 
> ## Conferences & Workshops
> 
> 1. [11th Symposium on Conformal and Probabilistic Prediction with Applications](http://copa-conference.com/)
> 2. [IFDS Workshop on Conformal Prediction](https://ifds.info/ifds-madlab-workshop) 🔥
> 3. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2022](https://sites.google.com/berkeley.edu/dfuq-22/home) 🔥
> 4. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373)🔥
> 5. [10th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2021/) 🔥
> 6. [9th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2020/) 🔥
> 7. [8th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2019/) 🔥
> 8. [7th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2018/) 🔥
> 9. [6th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2017/) 🔥
> 
> ## Python
> 
> 1. [MAPIE - Model Agnostic Prediction Interval Estimator](https://github.com/scikit-learn-contrib/MAPIE) by Quantmetry team (2021) [Paper](https://arxiv.org/pdf/2207.12274.pdf) [slides](https://arxiv.org/pdf/2207.12274.pdf) 🚀🔥
> 2. [Puncc (Predictive uncertainty calibration and conformalization)](https://github.com/deel-ai/puncc) [paper](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) 🔥
> 3. [‘Crêpes’ - Conformal regressors and predictive systems](https://github.com/henrikbostrom/crepes) by Henrik Boström (2021) [Paper](https://copa-conference.com/papers/COPA2022_paper_11.pdf) 🔥 [Presentation](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
> 4. [Python implementation of binary and multi-class Venn-ABERS calibration](https://github.com/ip200/venn-abers) by Ivan Petej (2023) [Paper] 🔥
> 5. [EnbPI](https://github.com/hamrel-cxu/EnbPI) by Chen Xu (2021) TIME SERIES 🚀🔥 [Paper](https://proceedings.mlr.press/v139/xu21h.html)
> 6. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥
> 7. [TorchCP - A library for conformal prediction](https://github.com/ml-stat-Sustech/TorchCP) 🔥
> 8. [Venn-ABERS Predictor](https://github.com/ptocca/VennABERS) by Paolo Toccaceli (2019) [Paper](https://proceedings.neurips.cc/paper/2015/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html) 🔥
> 9. [Conformalized Quantile Regression](https://github.com/yromano/cqr) by Yaniv Romano (2019) 🔥
> 10. [Conformal Classification](https://github.com/aangelopoulos/conformal_classification) by Anastasios N. Angelopoulos (2021)
> 11. [Orange3 Conformal Prediction](https://github.com/biolab/orange3-conformal)
> 12. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
> 13. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)
> 14. [TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch](https://github.com/TorchUQ/torchuq) (2022)
> 15. [Multi-class-probabilistic-classification using Venn-ABERS (Conformal) prediction](https://github.com/valeman/Multi-class-probabilistic-classification) by Valery Manokhin (Royal Holloway, 2022)
> 16. [Copula Conformal Multi Target Regression](https://github.com/M-Soundouss/CopulaConformalMTR) by Soundouss Messoudi (2021)
> 17. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)
> 18. [Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems](https://github.com/msesia/chr) by Mateo Sesia and Yaniv Romano (NeurIPS 2021). 🔥
> 19. [Conformalized density- and distance-based anomaly detection in time-series data (KNN-CAD)](https://github.com/numenta/NAB/tree/master/nab/detectors/knncad) by Evgeny Burnaev, Vladislav Ishimtsev (2016). Top #3 winning solution in Numenta competition 🔥
> 20. [Conformal time-series forecasting](https://github.com/kamilest/conformal-rnn) by Kamile ̇ Stankeviciute (Cambridge, NeurIPS 2021) TIME SERIES 🚀🔥
> 21. [Valid Prediction Intervals](https://github.com/nmdwolf/ValidPredictionIntervals/tree/main/Code) by Nicolas Dewolf, Bernard DeBaets, Willem Waegeman (2022) 🚀🔥
> 22. [Adaptive Conformal Predictions for Time Series](https://proceedings.mlr.press/v162/zaffran22a/zaffran22a.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ́ron, Yannig Goude, and Julie Josse (2022) TIME SERIES 🚀🔥 [Video](https://www.youtube.com/watch?v=Yuxu9aUpVi0) [Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
> 23. [Conformal learning from scratch](https://github.com/marharyta-aleksandrova/conformal-learning/blob/main/theory/conformal_learning_from_scratch.ipynb) by Marharyta Aleksandrova (2021)
> 24. [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression) Vilde Jensen, Filippo Maria Bianchi and Stian Norman Anfinsen (2022) TIME SERIES 🚀🔥
> 25. [Conformalized Online Learning: Online Calibration Without a Holdout Set](https://github.com/Shai128/rci) by Shai Feldman, Stephen Bates and Yaniv Romano (2022). TIME SERIES 🚀🔥
> 26. [PySloth - Python package for Probabilistic Prediction](https://github.com/PySloth/pysloth) by Valery Manokhin 🚀🔥
> 27. [Conformal Prediction in KNIME](https://hub.knime.com/tuwelofstrom/spaces/Predicting%20with%20Confidence/latest/~h8R_oa6iPRTssrYs/) by Tuwe Löfström and Redfield AB (2022)
> 28. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥
> 29. [SKTime](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.conformal.ConformalIntervals.html) by Franz Kiraly (2022)
> 30. [NeuralProphet](https://github.com/ourownstory/neural_prophet/blob/main/tutorials/feature-use/uncertainty_conformal_prediction.ipynb) (2022) 🚀🔥
> 31. [River](https://github.com/online-ml/river/blob/0506ad73e45403638f13d66c6b2d71920d307461/river/conf/jackknife.py#L9) 2022
> 32. [TorchUQ](https://github.com/TorchUQ/torchuq/blob/5335c5948385c7b9bde4baefc9399d79a7cb07ef/docs/tutorials/1_c_1_conformal/1_c_1_conformal.rst#L17) (2022)
> 33. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) [Paper](https://arxiv.org/pdf/2302.04019.pdf) by Amazon, (2022) TIME SERIES 🚀🔥
> 34. [Nixtla mlforecast](https://nixtla.github.io/mlforecast/docs/prediction_intervals.html#references) TIME SERIES 🚀🔥
> 35. [Nixtla statsforecast](https://nixtla.github.io/statsforecast/docs/tutorials/conformalprediction.html#introduction) TIME SERIES 🚀🔥
> 36. [https://github.com/mikekeith52/scalecast](https://github.com/mikekeith52/scalecast) TIME SERIES 🚀🔥
> 37. [Puncc (Predictive uncertainty calibration and conformalization)](https://github.com/deel-ai/puncc) 🔥
> 38. [plot\_utils - Plotting library for conformal prediction metrics, intended to facilitate fast testing in e.g. notebooks](https://github.com/pharmbio/plot_utils) 🔥
> 39. [calibrated-explanations - Calibrated Explanations for Machine Learning Models using Venn-Abers and Conformal Predictive Systems](https://github.com/Moffran/calibrated_explanations) by Helena Löfström (2023)
> 40. [Conformers - Unofficial Conformal Language Modelling library](https://github.com/Bradley-Butcher/Conformers/) 🚀
> 41. [Multi-class probabilistic classification using Venn-ABERS (Conformal) prediction](https://github.com/valeman/Multi-class-probabilistic-classification) by Valery Manokhin (2022) 🔥
> 42. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) 🔥
> 
> ## R
> 
> 1. [Conformal Prediction ih tidymodels](https://github.com/tidymodels/tidymodels.org/pull/23) by Max Kuhn (Posit/RStudio, 2023) [video](https://www.youtube.com/watch?v=3omi4lm1da0) 🔥
> 2. [Modeltime](https://github.com/business-science/modeltime/issues/173#issuecomment-1664681578) (2023) by Matt Dancho (Business Science, 2023) TIME SERIES 🔥
> 3. [Conformal prediction for 80+ classes of `R` models with the `marginaleffects` package](https://marginaleffects.com/articles/conformal.html) by Vincent Arel-Bundock (2023) 🔥
> 4. [AdaptiveConformal](https://github.com/herbps10/AdaptiveConformal) (2023) [paper](https://hal.science/hal-04316544/) 🔥
> 5. [Conformal Inference R Project](https://github.com/ryantibs/conformal) maintained by Ryan Tibshirani (2016) 🔥
> 6. [Prediction Bands](https://github.com/rizbicki/predictionBands) by Rafael Izbicki and Benjamin LeRoy (2019)
> 7. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
> 8. [Online Time Series Anomaly Detectors](https://github.com/valeman/otsad) by Alaine Iturria, 2021 🔥
> 9. [piRF - Prediction Intervals for Random Forests](https://github.com/chancejohnstone/piRF) by Chancellor Johnstone and Haozhe Zhang (2019)
> 10. [conformalClassification: Transductive and Inductive Conformal Predictions for Classification Problems](https://cran.r-project.org/web/packages/conformalClassification/) by Niharika Gauraha and Ola Spjuth (2019)
> 11. [R Package for Spatial Conformal Prediction](https://github.com/mhuiying/scp)
> 12. [conformalInference.multi: Conformal Inference Tools for Regression with Multivariate Response](https://cran.r-project.org/web/packages/conformalInference.multi/index.html) by Jacopo Diquigiovanni, Matteo Fontana, Aldo Solari, Simone Vantini, Paolo Vergottini, Ryan Tibshirani (2021) 🔥
> 13. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
> 14. [cfsurvival - An R package that implements the conformalized survival analysis methodology](https://zhimeir.github.io/cfsurvival/index.html) [Paper](https://arxiv.org/abs/2103.09763)
> 15. [ClusTorus: An R Package for Prediction and Clustering on the Torus by Conformal Prediction](https://journal.r-project.org/articles/RJ-2022-032/RJ-2022-032.pdf) by Seungki Hong and Sungkyu Jung (2022)
> 16. [conformal glm - conformal prediction for generalized linear regression models](https://github.com/DEck13/conformal.glm) by Daniel Eck (2019)
> 17. [caretForecast - Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms](https://github.com/Akai01/caretForecast)
> 18. [Localized Conformal Prediction - LCP](https://github.com/LeyingGuan/LCP)
> 19. [conformalbayes - Jackknife(+) Predictive Intervals for Bayesian Models](https://github.com/CoryMcCartan/conformalbayes) (2022)
> 20. [conformal.fd Conformal inference prediction regions for Multiple Functional response regression](https://github.com/paolo-vergo/conformal.fd) (2021)
> 
> ## Julia
> 
> 1. [ConformalPrediction.jl](https://github.com/pat-alt/ConformalPrediction.jl) by Patrick Altmeyer (2022) [Article - Conformal Prediction in Julia, Part I - Introduction](https://towardsdatascience.com/conformal-prediction-in-julia-351b81309e30) [Article - Conformal Prediction in Julia, Part II - How to conformalize a deep image classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) [Article - Conformal Prediction in Julia, Part III - Prediction intervals for any regression model](https://towardsdatascience.com/prediction-intervals-for-any-regression-model-306930d5ad9a)
> 2. [RandomForest](https://github.com/henrikbostrom/RandomForest) by Henrik Boström (2017) 🔥
> 
> ## Other Languages
> 
> 1. [LibCP – A Library for Conformal Prediction](https://github.com/fated/libcp) 🔥
> 2. [An Implementation of Venn-ABERS predictor](https://github.com/fated/venn-abers-predictor) 🔥
> 3. [LibVM – A Library for Venn Machine](https://github.com/fated/libvm)
> 4. [Scala-CP](https://github.com/mcapuccini/scala-cp) by Marco Capuccini (2017)’ 🔥 (see tutorial section ‘Conformal Prediction in Spark’)
> 
> ## AI platforms
> 
> 1. [Conformal Prediction in Knime](https://copa-conference.com/papers/COPA2022_paper_8.pdf) [Presentation](https://copa-conference.com/presentations/tuwe_presentation.pdf) 🔥
> 2. [Data Robot]([https://docs.datarobot.com/en/docs/release/public-preview/mlops-preview/prediction-intervals-regression.html/the-tru](https://docs.datarobot.com/en/docs/release/public-preview/mlops-preview/prediction-intervals-regression.html/the-tru) mpet-chart)
> 3. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) by Amazon, (2022) 🔥
> 4. [Microsoft Azure](https://learn.microsoft.com/en-gb/archive/blogs/machinelearning/anomaly-detection-using-machine-learning-to-detect-abnormalities-in-time-series-data)🔥
> 5. [Nixtla mlforecast](https://nixtla.github.io/mlforecast/docs/prediction_intervals.html#references) TIME SERIES 🚀🔥
> 6. [Nixtla statsforecast](https://nixtla.github.io/statsforecast/docs/tutorials/conformalprediction.html#introduction) TIME SERIES 🚀 🔥
> 7. [https://github.com/mikekeith52/scalecast](https://github.com/mikekeith52/scalecast) TIME SERIES 🚀🔥
> 8. [Puncc (Predictive uncertainty calibration and conformalization)](https://github.com/deel-ai/puncc) 🔥
> 9. [plot\_utils - Plotting library for conformal prediction metrics, intended to facilitate fast testing in e.g. notebooks](https://github.com/pharmbio/plot_utils) 🔥
> 10. [calibrated-explanations - Calibrated Explanations for Machine Learning Models using Venn-Abers and Conformal Predictive Systems](https://github.com/Moffran/calibrated_explanations) by Helena Löfström (2023)
> 11. [Conformers - Unofficial Conformal Language Modelling library](https://github.com/Bradley-Butcher/Conformers/) 🚀
> 12. [Multi-class probabilistic classification using Venn-ABERS (Conformal) prediction](https://github.com/valeman/Multi-class-probabilistic-classification) by Valery Manokhin (2022) 🔥
> 13. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) 🔥
> 
> ## R
> 
> 1. [Conformal Prediction ih tidymodels](https://github.com/tidymodels/tidymodels.org/pull/23) by Max Kuhn (Posit/RStudio, 2023) [video](https://www.youtube.com/watch?v=3omi4lm1da0) 🔥
> 2. [Modeltime](https://github.com/business-science/modeltime/issues/173#issuecomment-1664681578) (2023) by Matt Dancho (Business Science, 2023) TIME SERIES 🚀🔥
> 3. [Conformal prediction for 80+ classes of `R` models with the `marginaleffects` package](https://marginaleffects.com/articles/conformal.html) by Vincent Arel-Bundock (2023) 🔥
> 4. [AdaptiveConformal](https://github.com/herbps10/AdaptiveConformal) (2023) [paper](https://hal.science/hal-04316544/) 🔥
> 5. [Conformal Inference R Project](https://github.com/ryantibs/conformal) maintained by Ryan Tibshirani (2016) 🔥
> 6. [Prediction Bands](https://github.com/rizbicki/predictionBands) by Rafael Izbicki and Benjamin LeRoy (2019)
> 7. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
> 8. [Online Time Series Anomaly Detectors](https://github.com/valeman/otsad) by Alaine Iturria, 2021 🔥
> 9. [piRF - Prediction Intervals for Random Forests](https://github.com/chancejohnstone/piRF) by Chancellor Johnstone and Haozhe Zhang (2019)
> 10. [conformalClassification: Transductive and Inductive Conformal Predictions for Classification Problems](https://cran.r-project.org/web/packages/conformalClassification/) by Niharika Gauraha and Ola Spjuth (2019)
> 11. [R Package for Spatial Conformal Prediction](https://github.com/mhuiying/scp)
> 12. [conformalInference.multi: Conformal Inference Tools for Regression with Multivariate Response](https://cran.r-project.org/web/packages/conformalInference.multi/index.html) by Jacopo Diquigiovanni, Matteo Fontana, Aldo Solari, Simone Vantini, Paolo Vergottini, Ryan Tibshirani (2021) 🔥
> 13. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
> 14. [cfsurvival - An R package that implements the conformalized survival analysis methodology](https://zhimeir.github.io/cfsurvival/index.html) [Paper](https://arxiv.org/abs/2103.09763)
> 15. [ClusTorus: An R Package for Prediction and Clustering on the Torus by Conformal Prediction](https://journal.r-project.org/articles/RJ-2022-032/RJ-2022-032.pdf) by Seungki Hong and Sungkyu Jung (2022)
> 16. [conformal glm - conformal prediction for generalized linear regression models](https://github.com/DEck13/conformal.glm) by Daniel Eck (2019)
> 17. [caretForecast - Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms](https://github.com/Akai01/caretForecast)
> 18. [Localized Conformal Prediction - LCP](https://github.com/LeyingGuan/LCP)
> 19. [conformalbayes - Jackknife(+) Predictive Intervals for Bayesian Models](https://github.com/CoryMcCartan/conformalbayes) (2022)
> 20. [conformal.fd Conformal inference prediction regions for Multiple Functional response regression](https://github.com/paolo-vergo/conformal.fd) (2021)
> 
> ## Julia
> 
> 1. [ConformalPrediction.jl](https://github.com/pat-alt/ConformalPrediction.jl) by Patrick Altmeyer (2022) [Article - Conformal Prediction in Julia, Part I - Introduction](https://towardsdatascience.com/conformal-prediction-in-julia-351b81309e30) [Article - Conformal Prediction in Julia, Part II - How to conformalize a deep image classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) [Article - Conformal Prediction in Julia, Part III - Prediction intervals for any regression model](https://towardsdatascience.com/prediction-intervals-for-any-regression-model-306930d5ad9a)
> 2. [RandomForest](https://github.com/henrikbostrom/RandomForest) by Henrik Boström (2017) 🔥
> 
> ## Other Languages
> 
> 1. [LibCP – A Library for Conformal Prediction](https://github.com/fated/libcp) 🔥
> 2. [An Implementation of Venn-ABERS predictor](https://github.com/fated/venn-abers-predictor) 🔥
> 3. [LibVM – A Library for Venn Machine](https://github.com/fated/libvm)
> 4. [Scala-CP](https://github.com/mcapuccini/scala-cp) by Marco Capuccini (2017)’ 🔥 (see tutorial section ‘Conformal Prediction in Spark’)
> 
> ## AI platforms
> 
> 1. [Conformal Prediction in Knime](https://copa-conference.com/papers/COPA2022_paper_8.pdf) [Presentation](https://copa-conference.com/presentations/tuwe_presentation.pdf) 🔥
> 2. [Data Robot](https://docs.datarobot.com/en/docs/release/public-preview/mlops-preview/prediction-intervals-regression.html/the-trumpet-chart)
> 3. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) by Amazon, (2022) 🔥
> 4. [Microsoft Azure](https://learn.microsoft.com/en-gb/archive/blogs/machinelearning/anomaly-detection-using-machine-learning-to-detect-abnormalities-in-time-series-data)

## **ENJOY & HAPPY LEARNING!** ❤

---

<div class="post-metadata">

**Author:** ![leejiacheng1](https://onehack.st/user_avatar/onehack.st/leejiacheng1/32/94426_2.png) [@leejiacheng1](https://onehack.st/u/leejiacheng1)\
**Post date:** [January 13, 2024, 1:40am UTC](https://onehack.st/t/awesome-conformal-prediction-massive-resources-collection/272000/4 "2024-01-13T01:40:13Z")

</div>

Fantastic share, thanks a lot 👍