A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
# Awesome Conformal Prediction [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) [![DOI](https://zenodo.org/badge/436989758.svg)](https://zenodo.org/badge/latestdoi/436989758)

[Slack for Awesome Conformal Prediction](https://join.slack.com/t/awesomeconformalpred/shared_invite/zt-1bghkcotu-8wVXu2iFaCSM3Ot42qWzvA)
Please reach out to rahul_vishwakarma@icloud.com if you have issues with Slack link and need invite to the Slack.

The most comprehensive professionally curated resource on Conformal Prediction including the best tutorials, videos, books, papers, articles, courses, websites, conferences and open-source libraries code in Python, R and Julia.

I have created this resource after completing my PhD in Machine Learning specialising in Conformal Prediction supervised by the creator of Conformal Prediction Prof. Vladimir Vovk. The resources were meticulously collected since 2015 and after completing my PhD (thesis "Machine Learning for Probabilistic Prediction is available in the "Theses" section) I have decided to share them with the global machine learning community.

Conformal Prediction goes back to Kolmogorov's notion of randomness described in two papers : 1) Andrei Kolmogorov (1968). "Logical basis for information theory and probability theory." IEEE Transactions on Information Theory IT-14:662-664 and 2) Andrei Kolmogorov (1983). "Combinatorial foundations of information theory and the calculus of probabilities." Russian Mathematical Surveys 38(4):29-4.  

Conformal Prediction is no longer a niche area of research like it was just a few years ago, it has seen exponential growth during the last 2-3 years due to the work of amazing ambassadors of Conformal Prediction such as Prof. [Larry Wasserman](https://www.stat.cmu.edu/~larry/) in academia. There has been two tracks dedicated to conformal prediction at [ICML2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373) and [ICML2022](https://sites.google.com/berkeley.edu/dfuq-22/home) and key invited talk by Professor Emmanuel Candes 'Conformal Prediction in 2022' at [NeurIPS2022](https://slideslive.com/38996063/conformal-prediction-in-2022?ref=speaker-43789). The [main conference on Conformal Prediction (COPA)](https://copa-conference.com) has been running for over 11 years.

Awesome Conformal Prediction has been cited in the best book on Machine Learning (aka the machine learning 'bible') ["Probabilistic Machine Learning: Advanced Topics"](https://github.com/probml/pml2-book) by the leading research scientist at Google (over 80K Google Scholar citations) and Bestselling Machine Learning Book Author Kevin Murphy (starting at page 555).

![Probabilistic Machine Learning: Advanced Topics](Probabilistic%20Machine%20Learning%20Advanced%20Topics%20(Kevin%20Murphy).png)

I am actively promoting Conformal Prediction (because it is well Awesome) on social media incluidng [LinkedIn](https://www.linkedin.com/in/valeriy-manokhin-phd-mba-cqf-704731236/) and [Twitter](https://twitter.com/predict_addict). [ResearchGate](https://www.researchgate.net/profile/Valery-Manokhin) has all my research and I occasionally publish in [Medium](https://medium.com/@valeman) sharing insights from the data science trenches in the industry. Please feel free to connect and also help spread the word about Conformal Prediction.

[![Star History Chart](https://api.star-history.com/svg?repos=valeman/awesome-conformal-prediction&type=Date)](https://star-history.com/#valeman/awesome-conformal-prediction&Date)

Please star 🌟 the repo and spread the word. If you use the repository in a scientific publication, please ensure citing Awesome Conformal Prediction to help promote this amazing framework in academia and industry:

Manokhin, Valery. (2022). Awesome Conformal Prediction (v1.0.0). Zenodo. https://zenodo.org/record/6467205 https://doi.org/10.5281/zenodo.6467205 

Bibtex entry export https://zenodo.org/record/6467205/export/hx

@software{manokhin_valery_2022_6467205,
  author       = {Manokhin, Valery},
  title        = {Awesome Conformal Prediction},
  month        = apr,
  year         = 2022,
  note         = {{"If you use Awesome Conformal Prediction, please 
                   cite it as below."}},
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.6467205},
  url          = {https://doi.org/10.5281/zenodo.6467205}
}

[Slack for Awesome Conformal Prediction](https://join.slack.com/t/awesomeconformalpred/shared_invite/zt-1b07b5n1c-swtQDpoaBaWGxDl_Xjr4Tw)

Please reach out to rahul_vishwakarma@icloud.com if you have issues with Slack link and need invite to the Slack.

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----------------------------------------------------------------------------------------------------------------------------------------------------------
Why Conformal Prediction?

One of the most influential and celebrated machine learning researchers - Professor Michael I. Jordan:

'𝗖𝗼𝗻𝗳𝗼𝗿𝗺𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗶𝗱𝗲𝗮𝘀 𝗮𝗿𝗲 𝗧𝗛𝗘 𝗮𝗻𝘀𝘄𝗲𝗿 𝘁𝗼 𝗨𝗤 (𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻), 𝗜 𝘁𝗵𝗶𝗻𝗸 𝗶𝘁'𝘀 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗜 𝗵𝗮𝘃𝗲 𝘀𝗲𝗲𝗻 - 𝗶𝘁𝘀 𝘀𝗶𝗺𝗽𝗹𝗲, 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗮𝗯𝗹𝗲 𝗲𝘁𝗰.' (ICML 2021 UQ workshop). 🔥🔥🔥🔥🔥

One the most influential statistics Professors - Larry Wasserman (Carnegie Mellon): 

'𝗦𝗼 𝘁𝗵𝗲 𝗯𝗲𝗮𝘂𝘁𝘆 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗻𝗳𝗼𝗿𝗺𝗮𝗹 𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗵𝗼𝘄 𝘀𝗶𝗺𝗽𝗹𝗲 𝗶𝘁 𝗶𝘀 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗶𝘁 𝗶𝘀. 𝗦𝗼 𝗜 𝘁𝗵𝗶𝗻𝗸 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗶𝗱𝗲𝗮𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝘁𝗰𝗵 𝗼𝗻, 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗶𝗱𝗲𝗮𝘀 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝗽𝗿𝗲𝘁𝘁𝘆 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗮𝗻𝗱 𝐞𝐚𝐬𝐲 𝐭𝐨 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐩𝐢𝐜𝐭𝐮𝐫𝐞 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟 𝐮𝐬𝐢𝐧𝐠 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐫𝐞𝐚𝐬𝐨𝐧 𝐭𝐡𝐚𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐧𝐟𝐨𝐫𝐦𝐚𝐥 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧.' 🚀🚀🚀🚀🚀 

'Conformal inference methods are becoming all the rage in academia and industry alike. In a nutshell, these methods deliver exact prediction intervals for future observations without making any distributional assumption whatsoever other than having iid, and more generally, exchangeable data.'

Prof. Emmanual Candes (Stanford) - Neurips 2022 key talk.

https://slideslive.com/icml-2021/workshop-on-distributionfree-uncertainty-quantification


When prominent professors from the best research labs in the world say this about conformal prediction it is quite an endorsement.
----------------------------------------------------------------------------------------------------------------------------------------------------------
What about the industry one might ask - Conformal Prediction already for several years powers the main anomaly detection proposition in Microsoft Azure.

In 2021-2022 conformal prediction research experienced exponential growth in academia and with the availability of open-source libraries the industry is positioned to replicate this growth in the industry.


📢📢Industry take notice. The revolution in Uncertainty Quantification / Probabilistic Prediction / Forecasting is already here 📢📢  A big one  🔥🔥🔥🔥🔥


![modrian](mondrian.jpg) 

## [Table of Contents]()

* [Events](#Events)

* [Books](#Books)

* [PhD and MSc Theses](#Theses)

* [Videos](#Videos) 
 
* [Papers](#Papers)

* [Papers Time Series](#Papers_Time_Series)

* [Articles](#Articles)

* [Kaggle](#Kaggle)

* [Tutorials](#Tutorials)

* [Courses](#Courses)

* [Presentation_slides](#Presentation_slides)

* [Researchers](#Researchers)

* [Websites](#Websites)

* [Twitter](#Twitter)

* [TikTok](#TikTok)

* [Conferences](#Conferences)

* [Python](#Python)  

* [R](#R)

* [Julia](#Julia) 

* [Code Other Languages](#Code-Other)

* [AI platforms](#AI-platforms)

* [Patents](#Patents)

* [Miscellaneous](#Miscellaneous)

* [Contributing](#Contributing) 


## Events


## Books

1. [Algorithmic Learning in a Random World](https://link.springer.com/book/10.1007/b106715) by Vladimir Vovk and Alex Gammerman, also Glenn Shafer (2005). Second edition in progress. 🔥🔥🔥🔥🔥
2. [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) 🔥🔥🔥🔥🔥
3. [Conformal predictive distributions with kernels](https://link.springer.com/chapter/10.1007/978-3-319-99492-5_4) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Chapter in 'Braverman Readings in Machine Learning. Key Ideas from Inception to Current State', Springer, 2018). 🔥🔥🔥🔥🔥
4. [Confidence, Likelihood, Probability](https://www.cambridge.org/core/books/confidence-likelihood-probability/143A34F11FB3D6F611F78E27C6D2CA5A) by Tore Schweder and Nils Lid Hjort (University of Oslo, 2016). Not directly about Conformal Prediction but a great book about modern frequentist methods. 🔥🔥🔥🔥🔥
5. [Probabilistic Machine Learning: Advanced Topics](https://probml.github.io/pml-book/book2.html#toc) by Kevin Murphy (2022) has a chapter on conformal prediction. 🔥🔥🔥🔥🔥

## 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)
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)
10. [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)
11. [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)
12. [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)
13. [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).
14. [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)
15. [Model-free methods for multiple testing and predictive inference](https://searchworks.stanford.edu/view/13876512), PhD Thesis, Zhimei Ren (Stanford, 2021)
16. [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)
17. [Predictive Maintenance with Conformal and Probabilistic Prediction: A Commercial Case Study](https://www.jamesgammerman.com/files/Thesis.pdf) by James Gammerman (2022)
18. [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)
19. [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)
20. [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)
21. [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)
22. [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)


## 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 Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://www.youtube.com/watch?v=nql000Lu_iE&t=1786s) by Anastasios N. Angelopoulos and Stephen Bates (2021)
3. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (2020) 🔥🔥🔥🔥🔥
4. [Conformal Predication Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) 🔥🔥🔥🔥🔥
5. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020)
6. [Predicting with Confidence - Henrik Boström](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Boström (2016)
7. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sönströd, Tuve Löfström, and Henrik Boström (2021)
8. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (2020)
9. [A Tutorial on Conformal Prediction](https://jmlr.org/papers/v9/shafer08a.html) by Glenn Shafer and Vladimir Vovk (2008) 🔥🔥🔥🔥🔥
10. [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) 🔥🔥🔥🔥🔥
11. [Conformal Prediction in Spark](https://docs.google.com/presentation/d/1eD1vUJVR3nejyJZsOMfxL-NxrLy7K2UUMSAEMHYLfek/edit#slide=id.g2276269343_0_129) by Marco Capuccini (Uppsala University, 2017)
12. [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) 🔥🔥🔥🔥🔥
13. [An Introduction to Conformal Prediction](https://cml.rhul.ac.uk/copa2017/presentations/CP_Tutorial_2017.pdf) by Henrik Linusson (2017) 
🔥🔥🔥🔥🔥
14. [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) 
15. [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)
16. [Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://www.youtube.com/watch?v=usaHyuu2TzY) by Anastasios Angelopoulos (Berkeley, 2022)
17. [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) 🔥🔥🔥🔥🔥
16. [Beyond Conformal Prediction: Tutorial on Conformal Part 3](https://www.youtube.com/watch?v=37HKrmA5gJE) by Anastasios Angelopoulos and Stephen Bates (2022) 🔥🔥🔥🔥🔥
17. [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) 🔥🔥🔥🔥🔥
18. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, USC, 2022) 🔥🔥🔥🔥🔥
19. [Regression prediction intervals with MAPIE on Kaggle](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie) by Carl McBride Ellis, PhD
20. [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)

## Courses
1. [Course on Conformal Prediction](https://mindfulmodeler.substack.com/p/e-mail-course-on-conformal-prediction) - 5 week course starts soon! 🔥🔥🔥🔥🔥
2. [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)
3. [Topics in Modern Statistical Learning](https://github.com/dobriban/Topics-In-Modern-Statistical-Learning) by Edgar Dobriban (Wharton Business School, University of Pennsylvania, 2022)
4. [Theory of Statistics](https://candes.su.domains/teaching/stats300c/) Stanford Statistics course by Prof Emmanuel Candes (2022)
5. [36-708 Statistical Methods for Machine Learning](https://www.stat.cmu.edu/~larry/=sml/) Carnegie-Mellon course by Prof Larry Wasserman (2022)

## 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) 🔥🔥🔥🔥🔥
52. [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 🔥🔥🔥🔥🔥 🔥🔥🔥🔥🔥
53. [Black-box uncertainty - Anastasios Angelopoulos](https://www.youtube.com/watch?v=jW-mbsVgcIc) by Anastasios Angelopoulos (Berkeley, USA, 2021) 🔥🔥🔥🔥🔥 
54. [P.C. Mahalanobis Memorial Lectures 2020-21](https://www.isibang.ac.in/~statmath/pcm2020/) by Vladimir Vovk (Royal Holloway, UK, 2021)
55. [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)
56. [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).
57. [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)
58. [Recent progress in predictive inference](https://www.youtube.com/watch?v=FQxpdMME0qU) by Emmanuel Candes, Stanford University (2022)
59. [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)
60. [Calibrating probabilistic hierarchical forecasts with conformal predictions](https://www.youtube.com/watch?v=VNhgk8Q1VFg) by Daan Ferdinandusse (University of Amsterdam, 2022)
61. [Michael I. Jordan on Conformal Prediction](https://www.youtube.com/watch?v=kSGP4F_ZcBY) by Michael I. Jordan (Berkeley, 2022)
62. [Distribution-free Prediction: Exchangeability and Beyond](https://www.youtube.com/watch?v=HVZDLhW8Kxg) by Rina Foygel Barber (University of Chicago, 2022)
63. [Purdue Statistics Theme Seminar, Conformal Prediction in 2022](https://www.youtube.com/watch?v=pjhp1moPvO4) by Emmanuel Candes (Stanford, 2022)
64. [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)
65. [Robust and Equitable Uncertainty Estimation](https://www.youtube.com/watch?v=Iznj13jP9ag) by Aaron Roth(2022)
66. [Conformal prediction under feedback covariate shift for biomolecular design](https://www.youtube.com/watch?v=AOyDjBSQjhk) by Clara Wong-Fannjiang (Berkeley, 2022)
67. [Conformal prediction in 2022](https://slideslive.com/38996063/conformal-prediction-in-2022?ref=speaker-43789) invited talk by Emmanuel Candes at NeurIPS2022 🔥🔥🔥🔥🔥
68. [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) 🔥🔥🔥🔥🔥

## 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)
13. [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)
14. [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)
15. [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) 🔥🔥🔥🔥🔥
16. [Distributional conformal prediction](https://arxiv.org/pdf/1909.07889.pdf) by Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu (2021) 🔥🔥🔥🔥🔥
17. [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)
18. [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) 🔥🔥🔥🔥🔥
19. [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)
20. [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)
21. [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)
22. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
23. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (Yale, 2020)
24. [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)
25. [Binary classification of imbalanced datasets using conformal prediction](https://pubmed.ncbi.nlm.nih.gov/28135672/) by 
Ulf Norinder, Scott Boyer (2017)
26. [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)
27. [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)
28. [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)
29. [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)
30. [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) 🔥🔥🔥🔥🔥
31. [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)
32. [Aggregated Conformal Prediction](https://link.springer.com/chapter/10.1007%2F978-3-662-44722-2_25) by Lars CarlssonMartin EklundUlf Norinder (2014)
33. [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)
34. [Cross-Conformal Prediction with Ridge Regression](https://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_21) by Harris Papadopoulos (2015)
35. [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)
36. [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)
37. [Beyond the Basic Conformal Prediction Framework](https://www.sciencedirect.com/science/article/pii/B978012398537800002X?via%3Dihub) by Vladimir Vovk (2014)
38. [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)
39. [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)
40. [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)
41. [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)
42. [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)
43. [Strong validity, consonance, and conformal prediction](https://arxiv.org/abs/2001.09225) by Leonardo Cella and Ryan Martin (2020)
44. [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)
47. [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)
48. [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)
49. [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)
50. [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) 🔥🔥🔥🔥🔥
51. [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)
52. [Nested Conformal Prediction and the Generalized Jackknife](https://arxiv.org/pdf/1910.10562v1.pdf) by Arun Kuchibhotla and Aaditya Ramdas (Carnegie Mellon, 2019)
53. [Predictive inference with the jackknife+](https://arxiv.org/abs/1905.02928) by Rina Foygel Barber, Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani (2020) 🔥🔥🔥🔥🔥
54. [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) 🔥🔥🔥🔥🔥
55. [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) 🔥🔥🔥🔥🔥
56. [Exchangeability, Conformal Prediction, and Rank Tests](https://arxiv.org/pdf/2005.06095.pdf) by Arun Kuchibhotla (Carnegie Mellon, 2021)
57. [Conformal prediction with localization](https://arxiv.org/pdf/1908.08558.pdf) by Leying Guan (2020)
58. [Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction](https://pubmed.ncbi.nlm.nih.gov/30908915/) by Ulf Norinder, Fredrik Svensson 
59. [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)
60. [Computing Full Conformal Prediction Set with Approximate Homotopy](https://arxiv.org/pdf/1909.09365.pdf) by Eugene Ndiaye, Ichiro Takeuchi (2019)
61. [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)
60. [Efficient and minimal length parametric conformal prediction regions](https://export.arxiv.org/pdf/1905.03657) by Daniel Eck and Forrest Crawford (2019)
61. [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)
62. [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)
63. [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)
64. [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)
65. [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)  🔥🔥🔥🔥🔥
66. [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)
67. [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)
68. [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). 🔥🔥🔥🔥🔥
69. [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) 🔥🔥🔥🔥🔥
70. [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)
71. [Conformal Prediction using Conditional Histograms](https://papers.nips.cc/paper/2021/file/31b3b31a1c2f8a370206f111127c0dbd-Paper.pdf) by Matteo Sesia and Yaniv Romano (NeurIPS 2021 paper). 🔥🔥🔥🔥🔥
72. [Valid prediction intervals for regression problems](https://arxiv.org/abs/2107.00363) by Nicolas Dewolf, Bernard De Baets, Willem Waegeman (2021) 🔥🔥🔥🔥🔥
73. [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)
74. [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) 🔥🔥🔥🔥🔥
75. [Distribution-Free Federated Learning with Conformal Predictions](https://arxiv.org/pdf/2110.07661.pdf) by Charles Lu and Jayashree Kalpathy-Cramer (2022)
76. [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)
77. [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)
78. [Conformalized density- and distance-based anomaly detection in time-series data](https://arxiv.org/abs/1608.04585) by Evgeny Burnaev, Vladislav Ishimtsev (2016) 
79. [Predictive Inference with Weak Supervision](https://arxiv.org/pdf/2201.08315.pdf) by Maxime Cauchois, Suyash Gupta, Alnur Ali and John Duchi (Stanford, 2022)
80. [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)
81. [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).
82. [Conformal predictive distributions with kernels](https://arxiv.org/abs/1710.08894) by Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman (Royal Holloway, UK, 2017)
83. [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). 🔥🔥🔥🔥🔥
84. [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).
85. [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).
86. [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)
87. [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) 🔥🔥🔥🔥🔥
88. [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) 🔥🔥🔥🔥🔥
89. [Conformal predictive decision making](http://proceedings.mlr.press/v91/vovk18b/vovk18b.pdf) by Vladimir Vovk and Claus Bendtsen (2018).
90. [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)
91. [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)
92. [Conformal Prediction Sets with Limited False Positives](https://arxiv.org/pdf/2202.07650.pdf) by Adam Fisch, Tal Schuster, Tommi Jaakkola and Regina Barzilay (MIT / Google Research, 2022)
93. [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)
94. [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)
95. [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)
96. [Probabilistic Prediction in scikit-learn](https://www.diva-portal.org/smash/get/diva2:1603345/FULLTEXT01.pdf) by Sweidan, Dirar and Ulf Johansson. 🔥🔥🔥🔥🔥
97. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
98. [Valid model-free spatial prediction](https://arxiv.org/pdf/2006.15640.pdf) by Huiying Mao, Ryan Martin and Brian J Reich (2020)
99. [Conformal Prediction with Temporal Quantile Adjustments](https://arxiv.org/pdf/2205.09940.pdf) by Zhen Lin, Shubhendu Trivedi, Jimeng Sun (2022)  TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
100. [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
101. [Conformal prediction interval for dynamic time-series](https://proceedings.mlr.press/v139/xu21h.html) by Chen Xu, Yao Xie (Georgia Tech, 2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
102. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
103. [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)
104. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
105. [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)
106. [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)
107. [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)
108. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) [Python Code](hhttps://github.com/valeman/Ensemble-Regularized-Adaptive-Prediction-Set-ERAPS) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
109. [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)
110. [Model Agnostic Conformal Hyperparameter Optimization](https://arxiv.org/pdf/2207.03017.pdf) by Riccardo Doyle (Spotify, 2022)
111. [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)
112. [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)
113. [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)
114. [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) 🔥🔥🔥🔥🔥
115. [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)
116. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
117. [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
118. [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)
119. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥  
120. [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)  🔥🔥🔥🔥🔥
121. [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) 🔥🔥🔥🔥🔥
122. [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)
123. [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)
124. [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) 
125. [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://arxiv.org/pdf/2208.08401.pdf) by Isaac Gibbs and Emmanual Candes (Stanford, 2022) 🔥🔥🔥🔥🔥
126. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
127. [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)
128. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/papers/COPA2022_paper_10.pdf) by Amr Alkhatib, Henrik Bostroem and Ulf Johansson (2022)
129. [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)
130. [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)
131. [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)
132. [Conformal Prediciton beyond exchangeability](https://arxiv.org/abs/2202.13415) by Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani (2022)
133. [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)
134. [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) 
135. [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)
136. [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)
137. [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)
138. [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) 
139. [Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey](https://arxiv.org/abs/2209.03580) by Sophia Sun (UCLA, 2022)
140. [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)
141. [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)
142. [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)
143. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
144. [Batch Multivalid Conformal Prediction](https://arxiv.org/abs/2209.15145) by Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth (Stanford, university of Pensylvania, 2022)
145. [Selection by Prediction with Conformal p-values](https://arxiv.org/pdf/2210.01408.pdf) by Ying Jin1 and Emmanuel J. Candes, (Stanford, 2022]
146. [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)
147. [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)
148. [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)
149. [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)
150. [Predictive inference with feature conformal prediction](https://openreview.net/pdf?id=0uRm1YmFTu) (2022) 🔥🔥🔥🔥🔥
151. [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)
152. [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)
153. [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)
154. [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)
155. [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)
156. [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)
157. [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) 🔥🔥🔥🔥🔥
158. [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)
159. [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) 🔥🔥🔥🔥🔥
160. [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)
161. [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) 🔥🔥🔥🔥🔥
162. [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) 🔥🔥🔥🔥🔥
163. [Engineering Uncertainty Representations to Monitor Distribution Shifts](https://openreview.net/pdf?id=Koug1i2HpH) by Thomas Bonnier and  Benjamin Bosch (Société Générale, 2022)
164. [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) 🔥🔥🔥🔥🔥
165. [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) 🔥🔥🔥🔥🔥
166. [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)
167. [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)
168. [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)
169. [Conformal Loss-Controlling Prediction](https://arxiv.org/pdf/2301.02424.pdf) by Di Wang, Ping Wang, Zhong Ji, Xiaojun Yang, Hongyue Li (2023)
170. [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)
171. [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)
172. [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)
173. [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) 🔥🔥🔥🔥🔥
174. [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)
175. [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)
176. [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(

## 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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
9. [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)
10. [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)
11. [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)
12. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
13.  [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
14. [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)
15. [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)
16. [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)
17. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://openreview.net/pdf?id=jCdoLxMZxf) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
18. [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)
19. [Amazon Fortuna](https://aws-fortuna.readthedocs.io/en/latest/examples/enbpi_ts_regression.html)

## Presentation_slides
1. [Machine Learning for Probabilistic Prediction](https://github.com/valeman/awesome-conformal-prediction/blob/main/Machine%20Learning%20for%20Probabilistic%20Prediction.pdf) by Valery Manokhin, 2022
2. [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)
3. [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)
4. [What Can Conformal Inference Offer to Statistics?](https://lihualei71.github.io/Job_Talk_Lihua_Lei.pdf) by Lihua Lei, Stanford University
5. [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)
6. [Applications of Conformal Predictors](https://copa-conference.com/presentations/lars_ernst.pdf) by Ernst Ahlberg and Lars Carlsson (Stena Line, 2022)
7. [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)
8. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/presentations/amr.pdf) by Amr Alkhatib, Henrik Boström and Ulf Johansson (2022)
9. [Well-Calibrated Rule Extractors](https://copa-conference.com/presentations/Johansson_U_COPA2022_pres.pdf) by Ulf Johansson, Tuwe Löfström, Niclas Ståhl (2022)
10. [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) by Patrizio Giovannotti (2022)
11. [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)
12. [Conformal Prediction beyond exchangeability](https://copa-conference.com/presentations/conformal_UAI_tutorial_2.pdf) by Rina Foygel Barber (University of Chicago, 2022)
13. [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)
14. [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
15. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/pdf/2212.03463.pdf) by Chen Xu, Yao Xie (Georgia Tech, 2022)
16. [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)
17. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, 2022)
18. [Conformal Prediction: an Introduction](https://github.com/online-ml/river/files/10098389/Conformal_Prediction_Presentation.pdf) by Leo Andeol (2022)


## Researchers
1. [Vladimir Vovk](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), Joint Research Centre - European Commission
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

## 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)

## 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/)
2. [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)


## 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) 🔥🔥🔥🔥🔥 
2. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2022](https://sites.google.com/berkeley.edu/dfuq-22/home) 🔥🔥🔥🔥🔥 
3. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373)🔥🔥🔥🔥🔥 
4. [10th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2021/) 🔥🔥🔥🔥🔥
5. [9th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2020/) 🔥🔥🔥🔥🔥
6. [8th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2019/) 🔥🔥🔥🔥🔥
7. [7th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2018/) 🔥🔥🔥🔥🔥
8. [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) Includes TIME SERIES (EnbPI) 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
2. ['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)
3. [EnbPI](https://github.com/hamrel-cxu/EnbPI) by Chen Xu (2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥 [Paper](https://proceedings.mlr.press/v139/xu21h.html)
4. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥🔥🔥🔥🔥
5. [Venn-ABERS Predictor](https://github.com/ptocca/VennABERS) by Paolo Toccaceli (2019) [Paper](https://proceedings.neurips.cc/paper/2015/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html) 🔥🔥🔥🔥🔥
6. [Conformalized Quantile Regression](https://github.com/yromano/cqr) by Yaniv Romano (2019) 🔥🔥🔥🔥🔥
7. [Conformal Classification](https://github.com/aangelopoulos/conformal_classification) by Anastasios N. Angelopoulos (2021)
8. [Orange3 Conformal Prediction](https://github.com/biolab/orange3-conformal)
10. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
11. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)
12. [TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch](https://github.com/TorchUQ/torchuq) (2022)
13. [Multi-class-probabilistic-classification using Venn-ABERS (Conformal) prediction](https://github.com/valeman/Multi-class-probabilistic-classification) by Valery Manokhin (Royal Holloway, 2022) 
14. [Copula Conformal Multi Target Regression](https://github.com/M-Soundouss/CopulaConformalMTR) by Soundouss Messoudi (2021)
15. [Uncertainty Toolbox](https://github.com/uncertainty-toolbox/uncertainty-toolbox) by Youngseog Chung, Willie Neiswanger, Ian Char and Han Guo (2021)
16. [Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems](https://github.com/msesia/chr) by Mateo Sesia and Yaniv Romano (NeurIPS 2021). 🔥🔥🔥🔥🔥
17. [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 🔥🔥🔥🔥🔥
18. [Conformal time-series forecasting](https://github.com/kamilest/conformal-rnn) by Kamile ̇ Stankeviciute (Cambridge, NeurIPS 2021) TIME SERIES 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
19. [Valid Prediction Intervals](https://github.com/nmdwolf/ValidPredictionIntervals/tree/main/Code) by Nicolas Dewolf, Bernard DeBaets, Willem Waegeman (2022) 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
20. [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) 
21. [Conformal learning from scratch](https://github.com/marharyta-aleksandrova/conformal-learning/blob/main/theory/conformal_learning_from_scratch.ipynb) by Marharyta Aleksandrova (2021)
23. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
24. [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 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
25. [PySloth - Python package for Probabilistic Prediction](https://github.com/PySloth/pysloth) by Valery Manokhin 🚀🚀🚀🚀🚀 🔥🔥🔥🔥🔥
26. [Conformal Prediction in KNIME](https://hub.knime.com/tuwelofstrom/spaces/Public/latest/Conformal%20Prediction~kFOgjfgw9wuw1ULw/) 
27. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) 🔥🔥🔥🔥🔥
28. [SKTime](https://www.sktime.org/en/stable/api_reference/auto_generated/sktime.forecasting.conformal.ConformalIntervals.html?highlight=Conformal#sktime.forecasting.conformal.ConformalIntervals) by Franz Kiraly (2022)
29. [NeuralProphet](https://github.com/ourownstory/neural_prophet/blob/main/tutorials/feature-use/uncertainty_conformal_prediction.ipynb) (2022)
30. [River](https://github.com/online-ml/river/blob/0506ad73e45403638f13d66c6b2d71920d307461/river/conf/jackknife.py#L9) 2022
31. [TorchUQ](https://github.com/TorchUQ/torchuq/blob/5335c5948385c7b9bde4baefc9399d79a7cb07ef/docs/tutorials/1_c_1_conformal/1_c_1_conformal.rst#L17) (2022)
32. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) by Amazon, (2022)

## Code-R
1. [Conformal Inference R Project](https://github.com/ryantibs/conformal) maintained by Ryan Tibshirani (2016) 🔥🔥🔥🔥🔥
2. [Prediction Bands](https://github.com/rizbicki/predictionBands) by Rafael Izbicki and Benjamin LeRoy (2019)
3. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
4. [Online Time Series Anomaly Detectors](https://github.com/valeman/otsad) by Alaine Iturria, 2021 🔥🔥🔥🔥🔥
5. [piRF - Prediction Intervals for Random Forests](https://github.com/chancejohnstone/piRF) by Chancellor Johnstone and Haozhe Zhang (2019)
6. [conformalClassification: Transductive and Inductive Conformal Predictions for Classification Problems](https://cran.r-project.org/web/packages/conformalClassification/) by Niharika Gauraha and Ola Spjuth (2019)
7. [R Package for Spatial Conformal Prediction](https://github.com/mhuiying/scp)
8. [conformalInference.multi: Conformal Inference Tools for Regression with Multivariate Response](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) 🔥🔥🔥🔥🔥
9. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
10. [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)
11. [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 by Seungki Hong and Sungkyu Jung (2022)


## Code-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) 🔥🔥🔥🔥🔥

## Code-Other
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)

## Patents
1. Rahul Vishwakarma, Method and system for reliably forecasting storage disk failure. US 2021/0034450 A1 United States Patent and Trademark Office, Feb 2021
2. Rahul Vishwakarma, Analyzing Time Series Data for Sets of Devices Using Machine Learning Techniques. US 2021/0241929 A1 United States Patent and Trademark Office, Aug 2021
3. Rahul Vishwakarma, System and method for prioritizing and preventing backup failures. US 2021/0374568 A1 United States Patent and Trademark Office, Dec 2021


    
