Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/valeman/awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
https://github.com/valeman/awesome-conformal-prediction

List: awesome-conformal-prediction

awesome awesome-list conformal-prediction datascience deeplearning machine-learning machinelearning probability probability-distribution probability-distributions python r uncertainty uncertainty-estimation uncertainty-quantification

Last synced: about 1 month ago
JSON representation

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

Awesome Lists containing this project

README

        

# 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)

![Applied Conformal Prediction course](Applied_Conformal_Prediction_course.png)

**My course 'Applied Conformal Prediction is now opened for enrollment on Maven ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [enroll into the next cohort here](https://maven.com/valeriy-manokhin/applied-conformal-prediction), [register interest for upcoming cohorts and information here](https://maven.com/forms/2a53e5)**

**My book 'Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications' can be ordered on Amazon** [**Amazon USA ๐Ÿ‡บ๐Ÿ‡ธ**](https://www.amazon.com/gp/aw/d/1805122762), [**Amazon UK ๐Ÿ‡ฌ๐Ÿ‡ง**](https://www.amazon.co.uk/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon India ๐Ÿ‡ฎ๐Ÿ‡ณ**](https://www.amazon.in/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS), [**Amazon Germany ๐Ÿ‡ฉ๐Ÿ‡ช**](https://www.amazon.de/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/), [**Amazon France ๐Ÿ‡ซ๐Ÿ‡ท**](https://www.amazon.fr/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Spain ๐Ÿ‡ช๐Ÿ‡ธ**](https://www.amazon.es/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon Canada ๐Ÿ‡จ๐Ÿ‡ฆ**](https://www.amazon.ca/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Japan ๐Ÿ‡ฏ๐Ÿ‡ต**](https://www.amazon.co.jp/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/) [**Amazon Brazil ๐Ÿ‡ง๐Ÿ‡ท**](https://www.amazon.com.br/Practical-Applied-Conformal-Prediction-Python/dp/1805122762) [**Amazon Australia ๐Ÿ‡ฆ๐Ÿ‡บ**](https://www.amazon.com.au/dp/B0C2VLR5KS) [**Amazon Singapore ๐Ÿ‡ธ๐Ÿ‡ฌ**](https://www.amazon.sg/dp/1805122762) [**Amazon Sweden ๐Ÿ‡ธ๐Ÿ‡ช**](https://www.amazon.se/Practical-Applied-Conformal-Prediction-Python/dp/1805122762)

The book has reached #1 in Amazon Hot New Releases in multiple categories: "Probability and Statistics", "Machine Theory", "Python Programming" ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ Please rate the book on Amazon if you liked it.

![Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications](https://github.com/valeman/awesome-conformal-prediction/blob/main/Practical%20Guide%20to%20Applied%20Conformal%20Prediction%20Learn%20and%20apply%20the%20best%20uncertainty%20frameworks%20to%20your%20industry%20applications.jpg)

Due to free tier limits of Slack, [Slack for Awesome Conformal Prediction](https://join.slack.com/t/awesomeconformalpred) is in general application-only.

Fill the form to be considered for access. https://docs.google.com/forms/d/e/1FAIpQLScuNkOz1jq-Qt1OJ8oxsDCLZMdn8TkQluHllxk_GodPC8-w_w/viewform

[26-August-2024 update: Slack has been reopened temporarily (link valid for 30 days only, real names only as per LinkedIn)](https://join.slack.com/t/awesomeconformalpred/shared_invite/zt-2p9uwmurq-lezTVXVP28Lc8z1JblB3Cw)

Preference is given to Conformal Prediction researchers or practitioners who have published papers, articles, tutorials, code or made YouTube videos about Conformal Prediction.

**Discover the Ultimate Conformal Prediction Resource: All-in-One and Expertly Curated** ๐ŸŒŸ ๐ŸŒŸ ๐ŸŒŸ ๐ŸŒŸ ๐ŸŒŸ

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

This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing in Conformal Prediction under the supervision of its creator, Prof. Vladimir Vovk. Since 2015, I have painstakingly gathered these invaluable resources, and upon completing my PhD (my thesis, "Machine Learning for Probabilistic Prediction," can be found in the "Theses" section), I am thrilled to share my expertise with the global machine learning community. Immerse yourself in a professionally curated collection that has been honed through years of dedication and experience.

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 has transcended its niche status in just a few years, experiencing exponential growth thanks to the tireless efforts of renowned ambassadors like Prof. [Larry Wasserman](https://www.stat.cmu.edu/~larry/) in academia. It has taken center stage with dedicated tracks at [ICML2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373) and [ICML2022](https://sites.google.com/berkeley.edu/dfuq-22/home), as well as a captivating keynote address 'Conformal Prediction in 2022' at [NeurIPS2022](https://slideslive.com/38996063/conformal-prediction-in-2022?ref=speaker-43789) by Professor Emmanuel Candes. Furthermore, the [main conference on Conformal Prediction (COPA)](https://copa-conference.com) has enjoyed a successful run for over 11 years. Join the vibrant community at the forefront of this rapidly evolving field.

**Connect and Share the Excitement of Conformal Prediction**

I'm enthusiastically promoting the wonderful world of Conformal Prediction (because it truly is awesome) across various social media platforms, including [LinkedIn](https://www.linkedin.com/in/valeriy-manokhin-phd-mba-cqf-704731236/) and [Twitter](https://twitter.com/predict_addict). You can find all my research on [ResearchGate](https://www.researchgate.net/profile/Valery-Manokhin), and I occasionally share insights from the data science trenches in the industry on [Medium](https://medium.com/@valeman). I warmly invite you to connect with me and help spread the word about the fascinating field of 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)

**A Warm Invitation to Support and Share: Star the Repo and Spread the Word**

Please consider starring ๐ŸŒŸ the repo and sharing it with others who might be interested.

**Terms and conditionc fo academic papers to be listed on Awesome Conformal Prediction**

Academic papers that cite the repo will be automatically listed on the repo, whilst those that do not might be delisted at any time for breach of T&C. If your paper cites the repo but is missing from the list, please reach out to me directly.

This change aims to maintain a balance between providing valuable resources to the industry and ensuring proper attribution.

Additionally, weโ€™ll be curating the list of academic papers to focus on the most impactful work, reducing the overall number to avoid overwhelming readers and delist previous papers from the authors that have not cited the resource in the past or new papers. The focus will be on more impactful recent papers and core historic paper.

Your support is invaluable in advancing the awareness and appreciation of Conformal Prediction:

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}
}

Buy Me A Coffee

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

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

'๐—–๐—ผ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—น ๐—ฃ๐—ฟ๐—ฒ๐—ฑ๐—ถ๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐—ฑ๐—ฒ๐—ฎ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ง๐—›๐—˜ ๐—ฎ๐—ป๐˜€๐˜„๐—ฒ๐—ฟ ๐˜๐—ผ ๐—จ๐—ค (๐˜‚๐—ป๐—ฐ๐—ฒ๐—ฟ๐˜๐—ฎ๐—ถ๐—ป๐˜๐˜† ๐—พ๐˜‚๐—ฎ๐—ป๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป), ๐—œ ๐˜๐—ต๐—ถ๐—ป๐—ธ ๐—ถ๐˜'๐˜€ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐˜€๐˜ ๐—œ ๐—ต๐—ฎ๐˜ƒ๐—ฒ ๐˜€๐—ฒ๐—ฒ๐—ป - ๐—ถ๐˜๐˜€ ๐˜€๐—ถ๐—บ๐—ฝ๐—น๐—ฒ, ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐—น๐—ถ๐˜€๐—ฎ๐—ฏ๐—น๐—ฒ ๐—ฒ๐˜๐—ฐ.' (ICML 2021 UQ workshop). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

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

'๐—ฆ๐—ผ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐—ฎ๐˜‚๐˜๐˜† ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—ฐ๐—ผ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—น ๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐—ต๐—ผ๐˜„ ๐˜€๐—ถ๐—บ๐—ฝ๐—น๐—ฒ ๐—ถ๐˜ ๐—ถ๐˜€ ๐˜๐—ผ ๐—ฑ๐—ผ ๐—ถ๐˜ ๐—ฎ๐—ป๐—ฑ ๐—ต๐—ผ๐˜„ ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐—น ๐—ถ๐˜ ๐—ถ๐˜€. ๐—ฆ๐—ผ ๐—œ ๐˜๐—ต๐—ถ๐—ป๐—ธ ๐˜†๐—ผ๐˜‚ ๐—ธ๐—ป๐—ผ๐˜„ ๐—ถ๐—ฑ๐—ฒ๐—ฎ๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐—ฐ๐—ฎ๐˜๐—ฐ๐—ต ๐—ผ๐—ป, ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐—น ๐—ถ๐—ฑ๐—ฒ๐—ฎ๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐—ฎ๐—ฟ๐—ฒ ๐—ฝ๐—ฟ๐—ฒ๐˜๐˜๐˜† ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐—น ๐—ฎ๐—ป๐—ฑ ๐ž๐š๐ฌ๐ฒ ๐ญ๐จ ๐ข๐ฆ๐ฉ๐ฅ๐ž๐ฆ๐ž๐ง๐ญ ๐ญ๐ก๐š๐ญ ๐ฒ๐จ๐ฎ ๐œ๐š๐ง ๐ฉ๐ข๐œ๐ญ๐ฎ๐ซ๐ž ๐ฒ๐จ๐ฎ๐ซ๐ฌ๐ž๐ฅ๐Ÿ ๐ฎ๐ฌ๐ข๐ง๐  ๐ข๐ง ๐ซ๐ž๐š๐ฅ ๐š๐ฉ๐ฉ๐ฅ๐ข๐œ๐š๐ญ๐ข๐จ๐ง๐ฌ ๐š๐ซ๐ž ๐ญ๐ก๐ž ๐ซ๐ž๐š๐ฌ๐จ๐ง ๐ญ๐ก๐š๐ญ ๐ฉ๐ž๐จ๐ฉ๐ฅ๐ž ๐ฎ๐ฌ๐ข๐ง๐  ๐œ๐จ๐ง๐Ÿ๐จ๐ซ๐ฆ๐š๐ฅ ๐ฉ๐ซ๐ž๐๐ข๐œ๐ญ๐ข๐จ๐ง.' ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€

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

'๐“’๐“ธ๐“ท๐“ฏ๐“ธ๐“ป๐“ถ๐“ช๐“ต ๐“ฒ๐“ท๐“ฏ๐“ฎ๐“ป๐“ฎ๐“ท๐“ฌ๐“ฎ ๐“ถ๐“ฎ๐“ฝ๐“ฑ๐“ธ๐“ญ๐“ผ ๐“ช๐“ป๐“ฎ ๐“ซ๐“ฎ๐“ฌ๐“ธ๐“ถ๐“ฒ๐“ท๐“ฐ ๐“ช๐“ต๐“ต ๐“ฝ๐“ฑ๐“ฎ ๐“ป๐“ช๐“ฐ๐“ฎ ๐“ฒ๐“ท ๐“ช๐“ฌ๐“ช๐“ญ๐“ฎ๐“ถ๐“ฒ๐“ช ๐“ช๐“ท๐“ญ ๐“ฒ๐“ท๐“ญ๐“พ๐“ผ๐“ฝ๐“ป๐”‚ ๐“ช๐“ต๐“ฒ๐“ด๐“ฎ. ๐“˜๐“ท ๐“ช ๐“ท๐“พ๐“ฝ๐“ผ๐“ฑ๐“ฎ๐“ต๐“ต, ๐“ฝ๐“ฑ๐“ฎ๐“ผ๐“ฎ ๐“ถ๐“ฎ๐“ฝ๐“ฑ๐“ธ๐“ญ๐“ผ ๐“ญ๐“ฎ๐“ต๐“ฒ๐“ฟ๐“ฎ๐“ป ๐“ฎ๐”๐“ช๐“ฌ๐“ฝ ๐“น๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“ฒ๐“ท๐“ฝ๐“ฎ๐“ป๐“ฟ๐“ช๐“ต๐“ผ ๐“ฏ๐“ธ๐“ป ๐“ฏ๐“พ๐“ฝ๐“พ๐“ป๐“ฎ ๐“ธ๐“ซ๐“ผ๐“ฎ๐“ป๐“ฟ๐“ช๐“ฝ๐“ฒ๐“ธ๐“ท๐“ผ ๐”€๐“ฒ๐“ฝ๐“ฑ๐“ธ๐“พ๐“ฝ ๐“ถ๐“ช๐“ด๐“ฒ๐“ท๐“ฐ ๐“ช๐“ท๐”‚ ๐“ญ๐“ฒ๐“ผ๐“ฝ๐“ป๐“ฒ๐“ซ๐“พ๐“ฝ๐“ฒ๐“ธ๐“ท๐“ช๐“ต ๐“ช๐“ผ๐“ผ๐“พ๐“ถ๐“น๐“ฝ๐“ฒ๐“ธ๐“ท ๐”€๐“ฑ๐“ช๐“ฝ๐“ผ๐“ธ๐“ฎ๐“ฟ๐“ฎ๐“ป ๐“ธ๐“ฝ๐“ฑ๐“ฎ๐“ป ๐“ฝ๐“ฑ๐“ช๐“ท ๐“ฑ๐“ช๐“ฟ๐“ฒ๐“ท๐“ฐ ๐“ฒ๐“ฒ๐“ญ, ๐“ช๐“ท๐“ญ ๐“ถ๐“ธ๐“ป๐“ฎ ๐“ฐ๐“ฎ๐“ท๐“ฎ๐“ป๐“ช๐“ต๐“ต๐”‚, ๐“ฎ๐”๐“ฌ๐“ฑ๐“ช๐“ท๐“ฐ๐“ฎ๐“ช๐“ซ๐“ต๐“ฎ ๐“ญ๐“ช๐“ฝ๐“ช.'

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

Konrad Banachewicz, Principal Data Scientist | Kaggle Grandmaster | Author of the "Kaggle book" and "Machine Learning using Tensorflow Cookbook"

'๐•‹๐•™๐•š๐•ค ๐•ฃ๐•–๐•ก๐•  ๐•š๐•ค ๐•ข๐•ฆ๐•š๐•ฅ๐•–, ๐•ข๐•ฆ๐•š๐•ฅ๐•– ๐•ค๐•ก๐•–๐•”๐•ฅ๐•’๐•”๐•ฆ๐•๐•’๐•ฃ ๐•š๐•Ÿ๐••๐•–๐•–๐••.'

**An Impressive Endorsement: Conformal Prediction's Growing Appeal in Academia and Industry**

When highly respected professors from top research labs worldwide express their support for conformal prediction, it speaks volumes about its credibility and potential.

As for its industry applications, Conformal Prediction has already been powering Microsoft Azure's primary anomaly detection offering for several years. With exponential growth in academia during 2021-2022 and the increasing availability of open-source libraries, it's clear that the industry is poised for a similar surge in adoption.

๐Ÿ“ข๐Ÿ“ข Attention, industry professionals: The revolution in Uncertainty Quantification, Probabilistic Prediction, and Forecasting is here, and it's making waves! ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ Embrace the future of machine learning with Conformal Prediction.
What about the industry one might ask - Conformal Prediction already for several years powers the main anomaly detection proposition in Microsoft Azure.

![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)

* [Other Languages](#other-languages)

* [AI platforms](#ai-platforms)

* [Patents](#patents)

* [Miscellaneous](#miscellaneous)

* [Contributing](#contributing)

## Events

1. [Applied Conformal Prediction course starts in May 2024!](https://maven.com/forms/2a53e5) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [Kaggle competition - probabilistic forecasting I: Temperature](https://www.kaggle.com/competitions/probabilistic-forecasting-i-temperature/overview) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Books

1. [Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications](https://a.co/d/9guwCTm) by Valeriy Manokhin (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [**Amazon USA ๐Ÿ‡บ๐Ÿ‡ธ**](https://www.amazon.com/gp/aw/d/1805122762), [**Amazon UK ๐Ÿ‡ฌ๐Ÿ‡ง**](https://www.amazon.co.uk/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon India ๐Ÿ‡ฎ๐Ÿ‡ณ**](https://www.amazon.in/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS), [**Amazon Germany ๐Ÿ‡ฉ๐Ÿ‡ช**](https://www.amazon.de/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/), [**Amazon France ๐Ÿ‡ซ๐Ÿ‡ท**](https://www.amazon.fr/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Spain ๐Ÿ‡ช๐Ÿ‡ธ**](https://www.amazon.es/Practical-Guide-Applied-Conformal-Prediction/dp/1805122762), [**Amazon Canada ๐Ÿ‡จ๐Ÿ‡ฆ**](https://www.amazon.ca/Practical-Guide-Applied-Conformal-Prediction-ebook/dp/B0C2VLR5KS/), [**Amazon Japan ๐Ÿ‡ฏ๐Ÿ‡ต**](https://www.amazon.co.jp/Valeriy-Manokhin-ebook/dp/B0C2VLR5KS/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€
2. [Algorithmic Learning in a Random World](https://link.springer.com/book/10.1007/978-3-031-06649-8) by Vladimir Vovk and Alex Gammerman, also Glenn Shafer (2022). Second edition. ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ great theory based book, very math heavy, no applications, no code
3. [Conformal Prediction for Reliable Machine Learning](https://www.elsevier.com/books/conformal-prediction-for-reliable-machine-learning/balasubramanian/978-0-12-398537-8) by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk (2014) OLD BOOK, largely out of date, no code

## Theses
1. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/361515440_Machine_Learning_for_Probabilistic_Prediction_PhD_thesis_VALERY_MANOKHIN), PhD Thesis, Valery Manokhin (Royal Holloway, UK, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (over 6K reads on Researchgate alone)
2. [Conformal and Venn Predictors for large, imbalanced and sparse chemoinformatics data](https://pure.royalholloway.ac.uk/portal/files/41316291/2021toccacelipphd.pdf), PhD Thesis, Paolo Toccaceli (Royal Holloway, UK, 2021)
3. [Competitive online algorithms for probabilistic prediction](https://pure.royalholloway.ac.uk/portal/files/36216771/Thesis_Raisa.pdf), PhD Thesis, Raisa Dzhamtyrova (Royal Holloway, UK, 2020)
4. [Conformal Prediction and Testing under On-line Compression Models](https://pure.royalholloway.ac.uk/portal/files/20318074/2014fedorovavphd.pdf), PhD Thesis, Valentina Fedorova (Royal Holloway, UK, 2014) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [Adaptive Online Learning](https://pure.royalholloway.ac.uk/portal/files/17462972/adamskiy_thesis.pdf), PhD Thesis, Dmitry Adamskiy (Royal Holloway, UK, 2013)
6. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
23. [Nonconformity Measures and Ensemble Strategies - An Analysis of Conformal Predictor Efficiency and Validity](http://su.diva-portal.org/smash/get/diva2:1547120/FULLTEXT01.pdf), PhD thesis by Henrik Linusson (Stockholm University, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
24. [Determine and explain confidence in predicting violations on inland ships in the Netherlands](https://repository.tudelft.nl/islandora/object/uuid:e0a9bb58-98ab-4c00-96ae-8701046a8012) by Bakker, Paul (TU Delft, 2020)
25. [Machine Learning with Conformal Prediction for Predictive Maintenance tasks in Industry 4.0](https://www.diva-portal.org/smash/get/diva2:1765779/FULLTEXT01.pdf) by Shuzhou Liu, Mulahuko Mpova (Jรถnkรถping University, 2023).
26. [Benchmarking conformal prediction methods for time series regression](https://github.com/valeman/awesome-conformal-prediction/blob/main/BScThesis_DerckPrinzhorn.pdf) by Derck W.E. Prinzhorn (2023) [code](https://github.com/dweprinz/Benchmarking-conformal-prediction-methods-for-time-series-regression) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
27. [Conformal Prediction Methods in Finance](https://impa.br/wp-content/uploads/2022/11/Projeto_Final_Joao-Vitor-Romano.pdf) by Finance Joรฃo Vitor Romano (Instituto de Matemรกtica Pura e Aplicada, Brazil, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
28. [An Active Learning Approach for Reject Inference in Credit Scoring using Conformal Prediction Intervals on Real and Semi-Artificial Data](https://github.com/MaximilianSuliga/Conformal-Active-Learning-for-Reject-Inference) by Maximilian Suliga (Humboldt University of Berlin, 2023)
29. [Confidence Predictions in Pharmaceutical Sciences](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1787351&dswid=-7234) by Staffan Arvidsson McShane (Uppsala University, Sweden, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
30. [Conform With the Wind Processing short-term ensemble forecasts with conformal based methods
for probabilistic wind-speed forecasting](https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9130213&fileOId=9130294), MSc thesis by Simon Althoff (Lund University, 2023)
31. [Trustworthy explanations: Improved decision support through well-calibrated uncertainty quantification](https://hj.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=4&af=%5B%5D&searchType=SIMPLE&sortOrder2=title_sort_asc&query=Helena+Lรถfstrรถm&language=en&pid=diva2%3A1810440&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-3088), PhD thesis by Helena Lรถfstrรถm (Jรถnkรถping Universitu, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
32. [Data Augmentation and Conformal Prediction](https://dspace.mit.edu/handle/1721.1/151275), MSc thesis by Helen Lu (MIT, 2023)
33. [Conformal prediction and copula based methods for profile monitioring](https://www.politesi.polimi.it/handle/10589/214268), MSc Thesis by Niccolo' Donadini (Politecnico di Milano, 2023)
34. [Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries](https://www.diva-portal.org/smash/get/diva2:1575162/FULLTEXT01.pdf) Uppsala University (2021)
35. [Conformal prediction and beyond](https://gcastro-98.github.io/conformal-prediction-media/thesis.pdf), MSc thesis by Gerard CASTRO CASTILLO (University of Barcelona, 2024)
36. [A New Perspective on Uncertainty Techniques in Regression](https://epub.jku.at/obvulihs/content/titleinfo/10001240), MSc thesis by Alexander Krauck (Johannes Kepler University of Linz, 2024)
37. [Robust Conformal Prediction Using Privileged Information](https://arxiv.org/abs/2406.05405) by Shai Feldman, Yaniv Romano (Technion, 2024)
38. [CONFINE: Conformal Prediction for Interpretable Neural Networks](https://arxiv.org/abs/2406.00539) by Linhui Huang, Sayeri Lala, Niraj K. Jha (Princeton University, 2024)
39. [Conformal prediction and beyond](https://gcastro-98.github.io/conformal-prediction-media/thesis.pdf), MSc thesis by Gerard CASTRO CASTILLO (University of Barcelona, 2024) [code](https://github.com/gcastro-98/conformal-prediction)
40. [Conformal prediction and uncertainty quantification in recommender systems](https://diposit.ub.edu/dspace/bitstream/2445/214656/1/tfg_alvarado_chamartin_roberto.pdf) MSc thesis by Roberto Alvarado Chamatrin (University of Barcelona, 2024) [code](https://diposit.ub.edu/dspace/bitstream/2445/214656/2/codi.zip) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
41. [Machine learning-based anti-cancer drug treatment optimization](https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/38100) PhD thesis by Kerstin Lenhof (Saarland University, 2024)
42. [Towards Uncertainty-Aware Hardware Trojan Detection](https://www.proquest.com/openview/5ae29a4bcb3ee1bf63150579e82f4c4a/1?pq-origsite=gscholar&cbl=18750&diss=y) MSc thesis by Rahul Vishwakarma (2024)
43. [A Diagnostic and Prescriptive Conformal Prediction Framework: Applied to Sleep Disorders](https://dspace.mit.edu/handle/1721.1/155919) MSc thesis by Faduma Khalif (MIT, 2024)
44. [Reliable Time Series Forecasting Interval Forecasting for Time Series with Machine Learning Models and EnbPI](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1880090&dswid=9218), Independent thesis Advanced level, Wang, Xuanq (KTH,2024)
45. [Conformal Prediction in Limit Order Books: Calibration and Uncertainty Quantification of DeepLOB](https://github.com/Fabio-Rossi-Hub/Conformal-HFT/blob/main/Thesis_DRAFT.pdf) by Fabio Rossi, Imperial College (2024)

## Tutorials

1. [A Conformal Prediction tutorial, an introductive review of the basics](https://conformalpredictionintro.github.io) by Margaux Zaffran ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (2024)
2. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
3. [Predicting with Confidence - Henrik Bostrรถm](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Bostrรถm (2016) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
6. [Conformal Prediction: How to quantify uncertainty of machine learning models? ECAS-ENBIS courseโ€“ ENBIS 2023 Annual conference](https://mzaffran.github.io/assets/files/Talks/Tuto_CP_ENBIS_ECAS.pdf) by Margaux Zaffran ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (2023)
7. [A Tutorial on Conformal Prediction](https://jmlr.org/papers/v9/shafer08a.html) by Glenn Shafer and Vladimir Vovk (2008) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
8. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
9. [An Introduction to Conformal Prediction](https://cml.rhul.ac.uk/copa2017/presentations/CP_Tutorial_2017.pdf) by Henrik Linusson (2017)
๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
10. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
11. [Tutorial on Conformal Prediction Claire Boyer Assistant Professor, LPSM, Paris Sorbonne Universitรฉ ; Margaux Zaffran PhD Candidate, EDF, Inria, CMAP, ร‰cole Polytechnique](https://claireboyer.github.io/tutorial-conformal-prediction/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
12. [A Tutorial on Conformal Predictive Distributions](https://www.youtube.com/watch?v=FUi5jklGvvo&t=3s) by Paolo Toccaceli (2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
13. [Venn Predictors Tutorial](https://www.youtube.com/watch?v=KsQpkjl7u1w) by Ulf Johansson, Cecilia Sรถnstrรถd, Tuve Lรถfstrรถm, and Henrik Bostrรถm (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
14. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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. [Conformal Prediction and Venn Predictors A Tutorial on Predicting with Confidence](https://icdatascience.org/wp-content/uploads/2019/07/ICDATA_tutorial_2019_Johansson_U.pdf) by Ulf Johansson, Henrik Linusson, Tuve Lรถfstrรถm, Henrik Bostrรถm, Alex Gammerman (2019) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
17. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
18. [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)
19. [Conformal Prediction in Spark](https://docs.google.com/presentation/d/1eD1vUJVR3nejyJZsOMfxL-NxrLy7K2UUMSAEMHYLfek/edit#slide=id.g2276269343_0_129) by Marco Capuccini (Uppsala University, 2017)
20. [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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
21. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, USC, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
22. [Regression prediction intervals with MAPIE on Kaggle](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie) by Carl McBride Ellis, PhD
23. [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)
24. [Conformal Inference Tutorial](https://bkompa.github.io/2020/09/19/Conformal-Inference-Tutorial.html) by Ben Kompa (2020)
25. [Prediction intervals for any machine learning model - How to construct prediction intervals with the Jackknife+ using the MAPIE package](https://www.valencekjell.com/posts/2022-09-14-prediction-intervals/) by Kjell Jorner (ETH, 2022)
26. [Uncertainty Quantification (1): Enter Conformal Predictors](https://www.youtube.com/watch?v=xZbuFKWV5NA&t=25s) by Mahdi Torabi Rad (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
27. [Uncertainty Quantification (2): Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
28. [Uncertainty Quantification (3): From Full to Split Conformal Methods](https://www.youtube.com/watch?v=YigGJfsCjDk) by Mahdi Torabi Rad (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
29. [Conformal Prediction in Genomics](https://github.com/BiolApps/ConformalPrediction/tree/main) by BiolApps (2023)
30. [Conformal Prediction: A Visual Introduction](https://conformal-prediction.streamlit.app/) in [VISxAI](https://visxai.io/) by Mihir Agarwal, Lalit Chandra Routhu, Zeel B Patel and Nipun Batra (IIT Gandhinagar, IIT Patna, 2023)
31. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Courses
1. [Applied Conformal Prediction course starts in May 2024!](https://maven.com/forms/2a53e5) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
3. [Topics in Modern Statistical Learning](https://github.com/dobriban/Topics-In-Modern-Statistical-Learning) by Edgar Dobriban (Wharton School, University of Pennsylvania, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [Theory of Statistics](https://candes.su.domains/teaching/stats300c/) Stanford Statistics course by Prof Emmanuel Candes (2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [36-708 Statistical Methods for Machine Learning](https://www.stat.cmu.edu/~larry/=sml/) Carnegie-Mellon course by Prof Larry Wasserman (2022)
6. [Course on Conformal Prediction](https://mindfulmodeler.substack.com/p/e-mail-course-on-conformal-prediction) by Christoph Molnar (2022)
7. [Conformal Prediction - Advanced Topics in Statistical Learning, Spring 2023](https://www.stat.berkeley.edu/~ryantibs/statlearn-s23/lectures/conformal.pdf) by Ryan Tibshirani (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
8. [Conformal Methods for Efficient and Reliable Deep Learning](https://dspace.mit.edu/handle/1721.1/152788) by Adam, Fisch (MIT, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
9. [Conformal Prediction 101 - in Portuguese ๐Ÿ‡ต๐Ÿ‡น](https://github.com/gusbruschi13/Conformal-Prediction/tree/main/cp-101) by Gustavo Bruschi (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
9. [Conformal Prediction Tutorial](https://www.youtube.com/watch?v=0MsGri8nmJQ) by Henrik Linusson (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
10. [Henrik Linusson: Conformal Prediction](https://www.youtube.com/watch?v=lQxH-zXrOwI&t=1522s) by Henrik Linusson (2020)
11. [Predicting with Confidence - Henrik Bostrรถm](https://www.youtube.com/watch?v=eXU-64dwHmA) by Henrik Bostrรถm (2016)
12. [How to increase certainty in predictive modeling](https://www.youtube.com/watch?v=fQQP84yxCRs&t=1277s) by Emmanuel Candes (Stanford, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
13. [Recent Progress in Predictive Inference](https://www.youtube.com/watch?v=tY73G_UvkAE&t=833s) by Emmanuel Candes (Stanford, 2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Sรถnstrรถd, Tuwe Lรถfstrรถm, and Henrik Bostrรถm (2021)
20. [Mondrian conformal predictive distributions](https://www.youtube.com/watch?v=dHNZxw8WQrs) by Henrik Bostrรถm, Ulf Johansson, and Tuwe Lรถfstrรถm (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
21. [Calibrating Multi-Class Models](https://www.youtube.com/watch?v=uCnFgTjUYto) by Ulf Johansson, Tuwe Lรถfstrรถm, and Henrik Bostrรถm (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 Tomaลพ Hoฤevar and Blaลพ Zupan (2021)
24. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=ITJAR3fcNuI) by Anastasios Angelopoulos Berkeley, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
25. [Conformal Prediction and Distribution-Free Calibration](https://synapse.math.univ-toulouse.fr/s/KDcWmmU9j9zk0rm) by Aaditya Ramdas (Carnegie Mellon, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
26. [Reliable Diagnostics by Conformal Predictors](https://www.youtube.com/watch?v=zW3R-vbLw58) by Alexander Gammerman (Royal Holloway, UK, 2015)
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 โ€“ 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 (Jรถnkรถping University, Sweden, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
38. [Distribution-Free, Risk-Controlling Prediction Sets](https://www.youtube.com/watch?v=z8WDmD5D-I0) by Anastasios Angelopoulos (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Bostrรถm, Ulf Johansson and Tuwe Lรถfstrรถm (KTH Royal Institute of Technology, Sweden, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Bostrรถm (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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
47. [Ulf Johansson: Venn Predictors](https://www.youtube.com/watch?v=xxZOLo8wxe0&t=98s) by Ulf Johansson (Jรถnkรถping University, Sweden, 2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
53. [Black-box uncertainty - Anastasios Angelopoulos](https://www.youtube.com/watch?v=jW-mbsVgcIc) by Anastasios Angelopoulos (Berkeley, USA, 2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[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 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
69. [Paper Reading Group - Fortuna, a Library for Uncertainty Quantification](https://www.youtube.com/watch?app=desktop&v=S_QgP3jBCqw)
70. [CLIMB Evergreen talk with Emmanuel Candรจs: Conformal Inference when Data is not Exchangeable](https://www.youtube.com/watch?v=D60vtpnkhTU) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
71. [Algo Hour โ€“ Conformal Inference of Counterfactuals and Individual Treatment Effects | Lihua Lei](https://www.youtube.com/watch?v=COW2QNBmEMw&t=2s)
72. ['MoroccoAI webinar - Dr. Soundouss Messoudi - 'Confidence learning using conformal prediction'](https://www.youtube.com/watch?v=SypbjiBrt3Q)
73. [Emmanuel Candes - A Taste of Conformal Prediction](https://www.carmin.tv/en/video/a-taste-of-conformal-prediction) by Emmanuel Candes (2023)
74. [Foundations of Conformal Prediction - Full Conformal Predictors](https://www.youtube.com/watch?v=R1dnPAYGwnk) by Mahdi Torabi Rad (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
75. [Max Mergenthaler and Fede Garza - Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
76. [Uncertainty Quantification with the Fortuna library by Gianluca Detommaso (AWS)](https://www.youtube.com/watch?v=FEtD6lnsI58) by Gianluca Detommaso (Amazon, 2023)
77. [Quantifying Uncertainty in Time Series Forecasting](https://www.youtube.com/watch?v=Bj1U-Rrxk48) by Max Mergenthaler and Fede Garza (Nixtla, 2023)
78. [NISS/Merck Meetup on Conformal Inference: Advancing the Boundaries of Machine Learning 4.19.2023](https://www.youtube.com/watch?v=SRbqUae9-_o&t=5631s) (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
79. [Max Kuhn - The Post-Modeling Model to Fix the Model](https://www.youtube.com/watch?v=3omi4lm1da0) by Max Kuhn (2023)๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
80. [ISDFS Talk: Robots that ask for help: Conformal Prediction for LLM Planners](https://www.youtube.com/watch?v=WnQopZKJsTw) by Anirudha Majumdar (Princeton/DeepMind) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ(2023)
81. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://www.youtube.com/watch?v=xvXrPdPb3Ko) by Allen Z Ren (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
82. [Conformal Prediction & Complex Data Analytics] (https://www.youtube.com/watch?v=PbHsmPupFak) by Matteo Fontana (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
83. [Three Easy Steps to Understand Conformal Prediction (CP), Conformity Score, Python Implementation](https://www.youtube.com/watch?v=oqK6rM8fbkk) by (Dr. Data Science 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
84. [Rising Stars #8: Clara Wong-Fannjiang (Genentech) - Prediction-Powered Inference](https://www.youtube.com/watch?v=TlFpVpFx7JY)
85. [Rising Stars #10 - Special Series on Conformal Prediction: Isaac Gibbs (Stanford University) Conformal Inference with Conditional Guarantees](https://www.youtube.com/watch?v=rvYnR0FGxM4)
86. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://www.youtube.com/watch?v=QhvLaZ1aS6A) by Andreas Auer TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
87. [Introducing Sequential Predictive Conformal Inference (SPCI)](https://www.youtube.com/watch?v=NQHrTMZ2-XA&t=346) by Chen Xu (2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
88. [Conformal Prediction Intervals: Empowering Executives for Informed Decision](https://www.youtube.com/watch?v=3StianQJZTk&t=26s) by Matthew Kolakowski (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
89. [Conformal Inference with Tidymodels - posit::conf(2023)](https://www.youtube.com/watch?v=vJ4BYJSg734) by Max Kuhn (2023)
90. [ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles](https://www.youtube.com/watch?v=8PwDHAITuwU) by Sangdon Park (2023)
91. [Leveraging conformal prediction for calibrated probabilistic time series forecast](https://www.youtube.com/watch?v=--WcrDRtrYk) by Inge van den Ende (Dexter Energy, 2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
92. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://www.youtube.com/watch?v=Yq9cvqEk2K8&t=8s) by Kexin Huang (Stanford, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
93. [Selection by Prediction with Conformal p-values](https://www.youtube.com/watch?v=VHcCmkChwTk&t=5s) by Ying Jin (Stanford, 2023)
94. [Trustworthy Retrieval Augmented Chatbots Utilizing Conformal Predictors](https://www.youtube.com/watch?v=JnWXebWUEg4) by Shuo Li (UPenn, 2023).
95. [An introduction to conformal prediction - PyLadies Amsterdam](https://www.youtube.com/watch?v=QFtdTyIWrz8) by Inge van den Ende (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[code](https://github.com/pyladiesams/conformal-prediction-jan2024)
96. [Anushri Dixit - Planning with Confidence: Uncertainty Quantification for Safety-Critical Tasks](https://www.youtube.com/watch?v=hhSejSXde6U) (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
97. [Analรญtica acelerada con Shapelets y conformal prediction, ~48 mins onwards](https://www.youtube.com/watch?v=4HxTUzPyK9g&t=4854s) by Carl McBride Ellis (2024) [code](https://github.com/Carl-McBride-Ellis/talks) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
98. [Conformal Quantile Estimation in Economics](https://www.youtube.com/watch?v=6Brjt6aM140) by Martin Fankhauser (Bocconi, 2024)
99. [Big Data: 4th lecture (uncertainty in learning, big data for NLP)](https://www.youtube.com/watch?v=ikFkHig5o6g&list=PL8n4sZAL72Dn8GXuC0x9aXyiVGVAbPHz3&index=6) by Prof. Patrick Glauner (2024)
100. [Unveiling Precision: A Novel ML Framework for Accurate Probability Estimates by Abel and Edgar](https://www.youtube.com/watch?v=MuecMDKPqTQ&t=1672s), not CP per se, showing the critical importance of calibration in finance (2024)
101. [Autonomy Talks - Somil Bansal: Safety Assurances for Learning-Enabled Autonomous Systems](https://www.youtube.com/watch?v=3f8Q19-BqRo) by Somil Bansal (USC, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
102. [Interview with Vladimir Vovk on Conformal Inference](https://www.youtube.com/watch?v=J7o2WJX_xQE) (2024)
103. [Error Embraced: Making Trustworthy Scientific Decisions with Imperfect Predictions](https://www.youtube.com/watch?v=oqLq-DUEP3s) by Clara Wong-fannjiang (Genentech) (2024)
104. [Conformalized Interval Arithmetic with Symmetric Calibration](https://arxiv.org/abs/2408.10939) by Rui Luo, Zhixin Zhou (City University of Hong Kong, 2024)
105. [Robust Yet Efficient Conformal Prediction Sets](https://www.youtube.com/watch?v=b5I_R3S2SdI) by Soroush H. Zargarbash (2024)
106.

## 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
12. [Regression conformal prediction with random forests](https://link.springer.com/content/pdf/10.1007%2Fs10994-014-5453-0.pdf) by Ulf Johansson, Henrik Bostrรถm, Tuve Lรถfstrรถm 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. Karlรฉn, 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
16. [Distributional conformal prediction](https://arxiv.org/pdf/1909.07889.pdf) by Victor Chernozhukov, Kaspar Wรผthrich, Yinchu Zhu (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 โ€“ 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Lรถfstrรถm, Ulf Johansson and Henrik Bostrรถm (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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Sรฉbastien 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. Kรผhnl, 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 Lรกszlรณ Gyรถrfi and Harro Walk (2020)
50. [Concepts and Applications of Conformal Prediction in Computational Drug Discovery](https://arxiv.org/pdf/1908.03569.pdf) by Isidro Cortรฉs-Ciriano and Andreas Bender (2019) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Candรจs, Aaditya Ramdas, and Ryan Tibshirani (2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Candรจs, Lihua Lei and Zhimei Ren (2021) [R-Code](https://github.com/valeman/cfsurv_paper) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
66. [Random Forest Prediction Intervals](https://haozhestat.github.io/files/manuscript_RFIntervals_FinalVersion.pdf) by Haozhe Zhangโ€ , Joshua Zimmermanโ€ , Dan Nettletonโ€  and Daniel J. Nordmanโ€  (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). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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). [code](https://github.com/msesia/chr) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
72. [Valid prediction intervals for regression problems](https://arxiv.org/abs/2107.00363) by Nicolas Dewolf, Bernard De Baets, Willem Waegeman (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
73. [Application of conformal prediction interval estimations to market makersโ€™ 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Vennโ€“Abers predictors](https://proceedings.mlr.press/v60/manokhin17a.html) by Valery Manokhin (Royal Holloway, UK, 2017). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 [code](https://github.com/ajfisch/conformal-fp) (MIT / Google Research, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
93. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/pdf/2202.07282.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ฬron, Yannig Goude, and Julie Josse (EDF / INRIA / CMAP, France, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [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. ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
100. [Calibration of Natural Language Understanding Models with Vennโ€“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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
102. [Conformal prediction set for time-series](https://arxiv.org/abs/2206.07851) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
103. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ฬron, Yannig Goude, and Julie Josse (2022) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) [code](https://github.com/clu5/lumbar-conformal) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
112. [Conformal Off-Policy Prediction in Contextual Bandits](https://arxiv.org/pdf/2206.04405.pdf) by Muhammad Faaiz Taufiq, Jean-Franรงois 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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, Joรฃo 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Tomรฉ, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Joรฃo 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 Hรฅkan Wieslander , Philip J. Harrison, Gabriel Skogberg, Sonya Jackson, Markus Fridรฉn, Johan Karlsson, Ola Spjuth, and Carolina Wรคhlby (2021)
141. [Audioโ€“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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) [Video](https://www.youtube.com/watch?v=YEpvHsHCCvQ) [code](https://github.com/ying531/selcf_paper)
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://arxiv.org/pdf/2210.00173.pdf) by Jiaye Teng, Chuan Wen, Dinghuai Zhang,
Yoshua Bengio, Yang Gao, Yang Yuan (Tsinghua University, Mila - Quebec AI Institute, Shanghai Artificial Intelligence Laboratory, Shanghai Qi Zhi Institute, 2022) [code](https://github.com/AlvinWen428/FeatureCP) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Cleavelandโˆ—, 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ฬƒaki) 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Noahโ€™s Ark Lab Canada, NeurIPS 2022 paper) [Code](https://github.com/Lei-Ding07/Conformal_Quantile_Fairness) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
163. [Engineering Uncertainty Representations to Monitor Distribution Shifts](https://openreview.net/pdf?id=Koug1i2HpH) by Thomas Bonnier and Benjamin Bosch (Sociรฉtรฉ Gรฉnรฉrale, 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 โ€“ Aย 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) [code](https://github.com/aangelopoulos/ppi_py) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 Zhouโ€ , 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 Lindemannโ€ , Truong Nghiem, Rahul Mangharam, (2023)
179. [Accelerating difficulty estimation for conformal regression forests](https://link.springer.com/content/pdf/10.1007/s10472-017-9539-9.pdf) by Henrik Bostroem, Henrik Linusson, Tuve Loefstroem, Ulf Johansson (2017)
180. [Conformal prediction for exponential families and generalized linear models](https://arxiv.org/pdf/1905.03657v1.pdf)
181. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) by Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023) [Code](https://github.com/Sulam-Group/k-rcps)
182. [Localized Conformal Prediction: A Generalized Inference Framework to Conformal Prediction](https://arxiv.org/pdf/2106.08460.pdf) [Code](https://github.com/LeyingGuan/LCPexperiments) [R package](https://github.com/LeyingGuan/LCP)
183. [From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling](https://arxiv.org/pdf/2302.05304.pdf) by Ernsting et.al. (2023)
184. [Conformal prediction for STL runtime verification](https://arxiv.org/pdf/2211.01539.pdf) by Lars Lindemann, Xin Qin, Jyotirmoy V. Deshmukh, George J. Pappas (University of Pennsylvania/University of Southern California, 2022)
185. [Adaptive Conformal Prediction for Motion Planning among Dynamic Agents](https://arxiv.org/pdf/2212.00278.pdf) by Anushri Dixit, Lars Lindemann, Skylar Wei, Matthew Cleaveland, George J Pappas, Joel W Burdick (California Institute of Technology/University of Pennsylvania, 2022)
186. [Classification with Valid and Adaptive Coverage](https://proceedings.neurips.cc/paper/2020/file/244edd7e85dc81602b7615cd705545f5-Paper.pdf) [Code](https://github.com/msesia/arc) by Yaniv Romano, Matteo Sesia, Emmanuel Candes (Neurips, 2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
187. [Risk Control for Online Learning Models](https://openreview.net/forum?id=uqLDy0HGPR7) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (2023)
189. [Derandomized novelty detection with FDR control via conformal e-values](https://arxiv.org/pdf/2302.07294v1.pdf) by Meshi Bashari, Amir Epstein, Yaniv Romano, and Matteo Sesia (2023) [code](https://github.com/Meshiba/derandomized-novelty-detection)
190. [Sensititivty analysis of individual treatment effects: A robust conformal inference approach](https://www.pnas.org/doi/epdf/10.1073/pnas.2214889120) [Code](https://github.com/ying531/cfsensitivity_paper) by Ying Jin, Zhimei Ren and Emmanual Candes (2023)
191. [Improving Adaptive Conformal Prediction Using Self-Supervised Learning](https://arxiv.org/abs/2302.12238) by Nabeel Seedat, Alan Jeffares, Fergus Imrie and Mihaela van der Schaar (Cambridge, 2023) [Video](https://www.youtube.com/watch?v=pagxatn7xNw) [Code](https://github.com/seedatnabeel/SSCP)
192. [Learning by Transduction - of the of earliest conformal prediction papers] (https://dl.acm.org/doi/10.5555/2074094.2074112#sec-comments) by Alex Gammerman, Vladimir Vovk and Vladimir Vapnik (Royal Holloway, University of London, 1998) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
193. [Hedging Predictions in Machine Learning](https://arxiv.org/pdf/cs/0611011.pdf) by
Alexander Gammerman and Vladimir Vovk (2008) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
194. [Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction](https://www.mdpi.com/2218-273X/8/3/85) by Ulf Norinder, Glenn Myatt and Ernst Ahlberg
195. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
196. [Fortuna: A Library for Uncertainty Quantification in Deep Learning](https://arxiv.org/abs/2302.04019) by Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau (2023)
197. [Conformal inference is (almost) free for neural networks trained with early stopping](https://arxiv.org/abs/2301.11556) by Ziyi Liang, Yanfei Zhou, Matteo Sesia (2023)
198. [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://arxiv.org/pdf/2212.03710.pdf) by Mahmoud Shoush and Marlon Dumas (University of Tartu,2022)
199. [Machine-Learning Applications of Algorithmic Randomness](https://eprints.soton.ac.uk/258960/1/Random_ICML99.pdf) by Vladimir Vovk, Alex Gammerman and Craig Saunders (1999) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
200. [On the universal distribution of the coverage in split conformal prediction](https://arxiv.org/abs/2303.02770) by Paulo C. Marques F. (2023)
201. [Lightweight, Uncertainty-Aware Conformalized Visual Odometry](https://arxiv.org/pdf/2303.02207.pdf) by Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula, and Amit Ranjan Trivedi (University of Illinois Chicago, 2023)
202. [Group conditional validity via multi-group learning](https://arxiv.org/abs/2303.03995v1) by Samuel Deng, Navid Ardeshir, Daniel Hsu (Columbia University, 2023)
203. [Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis](https://arxiv.org/pdf/2303.10694.pdf) by Subhankar Ghosh, Taha Belkhouj, Yan Yan, Janardhan Rao Doppa (Washington State University, 2023)
204. [Mondrian conformal regressors](https://proceedings.mlr.press/v128/bostrom20a.html) by Henrik Bostrรถm, Ulf Johansson (2020)
205. [Mondrian Conformal Predictive Distributions](https://proceedings.mlr.press/v152/bostrom21a/bostrom21a.pdf) by Henrik Bostrรถm, Ulf Johansson,
Tuwe Lรถfstrรถm (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
206. [Adaptive Conformal Prediction by Reweighting Nonconformity Score](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou, Nicolas J.B Brunel (2023) [Code](https://github.com/salimamoukou/ACPI)
207. [Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation](https://arxiv.org/pdf/2303.12246.pdf) by Heng Yang and Marco Pavone (NVIDIA, 2023)
208. [A Two-Sample Conditional Distribution Test Using Conformal
Prediction and Weighted Rank Sum](https://arxiv.org/pdf/2010.07147.pdf) by Xiaoyu Hu and Jing Lei (Peking University and Carnegie Mellon University, 2023)
209. [Conformalized Semi-Supervised Random Forest For Classification and Abnormality Detection](https://arxiv.org/abs/2302.02237) (2023)
210. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/abs/2302.03791) Jacopo Teneggi, Matt Tivnan, J Webster Stayman, Jeremias Sulam (John Hopkins University, 2023)
211. [Safe Perception-Based Control under Stochastic Sensor
Uncertainty using Conformal Prediction](https://arxiv.org/pdf/2304.00194.pdf) by Shuo Yang, George J. Pappas, Rahul Mangharam, and Lars Lindemann (University of Pennsylvania, 2023)
212. [Conformal Prediction Regions for Time Series using Linear Complementarity Programming](https://arxiv.org/pdf/2304.01075.pdf) by Matthew Cleaveland, Insup Lee, George J. Pappasโ€ , and Lars Lindemann (University of Pennsylvania, 2023)
213. [Development and Evaluation of Conformal Prediction Methods for QSAR](https://arxiv.org/pdf/2304.00970.pdf) by Yuting Xua, Andy Liawa, Robert P. Sheridan (Merck, 2023)
214. [Multi-Agent Reachability Calibration with Conformal Prediction](https://arxiv.org/pdf/2304.00432v1.pdf) by Anish Muthali1, Haotian Shen, Sampada Deglurkar, Michael H. Lim, Rebecca Roelofs, Aleksandra Faust, Claire Tomlin (University of Berkeley, 2023)
215. [Conformalized Unconditional Quantile Regression](https://arxiv.org/pdf/2304.01426.pdf) by Ahmed M. Alaa, Zeshan Hussain, David Sontag (Berkeley/MIT, 2023).
216. [Conformal Off-Policy Evaluation in Markov Decision Processes](https://arxiv.org/pdf/2304.02574v1.pdf) by
Daniele Foffano, Alessio Russo and Alexandre Proutiere (KTH, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
217. [Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning](https://arxiv.org/pdf/2304.03398.pdf) by Sangwoo Park and Osvaldo Simeone (2023) [Code](https://github.com/kclip/quantum-CP) QuantumML ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
218. [Probabilistic prediction with locally weighted jackknife predictive system](https://link.springer.com/content/pdf/10.1007/s40747-023-01044-0.pdf#page18) by Di Wang, Ping Wang, Pingping Wang, Cong Wang, Zhen He, Wei Zhang ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
219. [Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision](https://arxiv.org/abs/2304.06158) by Siddhaarth Sarkar, Arun Kumar Kuchibhotla (2023)
220. [Conformal Regression in Calorie Prediction for Team Jumbo-Visma](https://arxiv.org/abs/2304.03778) by Kristian van Kuijk, Mark Dirksen, Christof Seiler (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[slides](https://copa-conference.com/presentations/Kristian.pdf)
221. [Design-based conformal prediction](https://arxiv.org/pdf/2303.01422.pdf) by Jerzy Wieczorek (2023) [code](https://github.com/ColbyStatSvyRsch/surveyConformal-paper-code) ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
222. [Inductive Confidence Machines for Regression](https://link.springer.com/content/pdf/10.1007/3-540-36755-1_29.pdf) by
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman (2002) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
223. [Model-Agnostic Nonconformity Functionsfor Conformal Classification](https://ieeexplore.ieee.org/document/7966105) by Ulf Johansson, Henrik Linusson, Tuve Lรถfstrรถm, Henrik Bostrรถm (2017) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
224. [Impact of model-agnostic nonconformity functions on efficiency of conformal classifiers: an extensive study](https://proceedings.mlr.press/v152/aleksandrova21a.html) by Marharyta Aleksandrova, Oleg Chertov (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
225. [Inductive Conformal Prediciton: A Straightforward Introduction with examples in Python](https://arxiv.org/pdf/2206.11810.pdf) by Martim Sousa (2022) [Code](https://github.com/Quilograma/ConformalPredictionTutorial/blob/main/Conformal%20Prediction.ipynb) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
226. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://stanfordasl.github.io/wp-content/papercite-data/pdf/Sinha.Pavone.CDC23.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Standord, 2023)
227. [Calibrated Explanations: with Uncertainty Information and Counterfactuals](https://arxiv.org/pdf/2305.02305.pdf) by Helena Lรถfstrรถm, Tuwe Lรถfstrรถm, Ulf Johansson, Cecilia S ฬˆonstr ฬˆod (2023) [Code](https://github.com/Moffran/calibrated_explanations) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
228. [Optimizing Hyperparameters with Conformal Quantile Regression](https://arxiv.org/pdf/2305.03623.pdf) by David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau (Amazon Science, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
229. [Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens](https://chemrxiv.org/engage/chemrxiv/article-details/6456778807c3f02937503688) by Andreas Luttens, Israel Cabeza de Vaca, Leonard Sparring, Ulf Norinder, Jens Carlsson (2023) [Code](https://github.com/carlssonlab/conformalpredictor) [Datasets](https://zenodo.org/record/7903161) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
230. [Predicting skin sensitizers with confidence โ€” Using conformal prediction to determine applicability domain of GARD](https://www.sciencedirect.com/science/article/pii/S0887233318300237?via%3Dihub) by Andy Forreryd, Ulf Norinder, Tim Lindberg, Malin Lindstedt (2018) ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
231. [Confidence-based Prediction of Antibiotic Resistance at the Patient-level Using Transformers](https://www.biorxiv.org/content/10.1101/2023.05.09.539832v1.full.pdf) by J.S. Inda-Diaz, A. Johnning, M. Hessel, A. Sjo ฬˆberg, A. Lokrantz, L. Hellda, M. Jirstrand, L. Svensson and E. Kristiansson (Chalmers University of Technology and University of Gothenburg/Centre for Antibiotic Resistance Research (CARe), 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
232. [Framework based on conformal predictors and power martingales for detection of fixed football matches](https://www.semanticscholar.org/paper/Framework-based-on-conformal-predictors-and-power-Zhuk-Chertov/9405c6a70f5ce211afa575b4bdc4cddb449e9594?utm_source=alert_email&utm_content=AuthorCitation&utm_campaign=AlertEmails_DAILY&utm_term=LibraryFolder+AuthorCitation&email_index=1-0-3&utm_medium=15942999) by I. Zhuk, O. Chertov (2023)
233. [Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems](https://arxiv.org/pdf/2305.10124.pdf) by Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
234. [Conformalized matrix completion](https://arxiv.org/pdf/2305.10637.pdf) by Yu Gui, Rina Foygel Barber, and Cong Ma (University of Chicago, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/yugjerry/conf-mc)
235. [Conformal Prediction With Conditional Guarantees](https://arxiv.org/pdf/2305.12616.pdf) by Isaac Gibbs, John Cherian, Emmanuel Candes (Stanford, 2023) [code](https://github.com/jjcherian/conditional-conformal)
236. [Uncertainty Quantification over Graph with Conformalized Graph Neural Networks](https://arxiv.org/abs/2305.14535) by Kexin Huang, Ying Jin, Emmanuel Candes, Jure Leskovec (Stanford, 2023) [code](https://github.com/snap-stanford/conformalized-gnn) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
237. [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://arxiv.org/pdf/2305.17564.pdf) by Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar (MIT/Berkeley, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) [video](https://www.youtube.com/watch?v=Ess8_S4avW4) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
240. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/pdf/2305.18404.pdf) by Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (Harvard/MIT, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
241. [Conformal Predictive Distribution Trees](https://link.springer.com/content/pdf/10.1007/s10472-023-09847-0.pdf) by Ulf Johansson, Tuwe Lรถfstrรถm, Henrik Bostrรถm (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
242. [CONFORMAL PREDICTION WITH PARTIALLY LABELED DATA](https://arxiv.org/pdf/2306.01191.pdf) by Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hรผllermeier (2023)
243. [Conformal Prediction for Federated Uncertainty Quantification Under Label Shift](https://arxiv.org/pdf/2306.05131.pdf) by Vincent Plassie, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov (2023)
244. [Conformalizing Machine Translation Evaluation](https://arxiv.org/pdf/2306.06221.pdfm) by Chrysoula Zerva, Andrรฉ F. T. Martins (2023)
245. [Class-Conditional Conformal Prediction With Many Classes](https://arxiv.org/pdf/2306.09335.pdf) by Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (Berkeley, 2023)
246. [Conformal Prediction Sets for Graph Neural Networks](https://openreview.net/pdf/1915b7fb247001d17114b5908727ac0f7056abe8.pdf) by Soroush Zargarbashi, Simone Antonelli, Aleksandar Bojchevski [Code](https://github.com/bhaweshiitk/ConformalLLM)
247. [Conformal link prediction to control the error rate](https://arxiv.org/pdf/2306.14693v1.pdf) by Ariane Marandon (2023)
248. [JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction
Under Standard and Feedback Covariate Shift](https://openreview.net/pdf?id=ORxBEWMPAJ) Drew Prinster, Suchi Saria, Anqi Liu (John Hopkins University, 2023) [Code](https://github.com/drewprinster/jaws-x) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
249. [Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction](https://arxiv.org/pdf/2306.17815.pdf) by Yunchuan Zhang, Sangwoo Park and Osvaldo Simeone (2023)
250. [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://arxiv.org/abs/2307.01928) by Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar (Princeton/DeepMind, 2023) [Website](https://robot-help.github.io) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
251. [Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery](https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-018-0325-4.pdf) by Nicolas Bosc, Francis Atkinson, Eloy Felix, Anna Gaulton, Anne Hersey and Andrew R. Leach (Cambridge, 2019) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
252. [Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers](https://www.diva-portal.org/smash/get/diva2:1163353/FULLTEXT01.pdf) by Henrik Linusson, Ulf Johansson, Henrik Bostroem, and Tuve Loefstroem (2014)
253. [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://arxiv.org/pdf/2302.03791.pdf) [Code](https://github.com/Sulam-Group/k-rcps) (John Hopkins University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
254. [Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors](https://mdpi-res.com/d_attachment/applsci/applsci-13-08647/article_deploy/applsci-13-08647.pdf?version=1690443630) by Gang Wang, Zhiying Lu, Ping Wang, Shuo Zhuang and Di Wang (2023)
255. [Plug-in martingales for testing exchangeability on-line](https://icml.cc/2012/papers/808.pdf) by Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk (Royal Holloway, UK, ICML 2012) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
256. [Testing Exchangeability On-Line](https://aaai.org/papers/100-testing-exchangeability-on-line/) by Vladimir Vovk, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, UK, ICML 2003) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
257. [Predictive Inference Is Free with the Jackknife+-after-Bootstrap](https://proceedings.neurips.cc/paper/2020/file/2b346a0aa375a07f5a90a344a61416c4-Paper.pdf) by Byol Kim, Chen Xu, Rina Foygel Barber (University of Chicago, 2020) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
258. [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://arxiv.org/abs/2305.18404) by Bhawesh Kumar, Charles Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam (MIT, 2023) [code](https://github.com/bhaweshiitk/ConformalLLM) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
259. [CONFORMAL PREDICTIONS ENHANCED EXPERT-GUIDED MESHING WITH GRAPH NEURAL NETWORKS](https://arxiv.org/pdf/2308.07358.pdf) [code](https://github.com/ahnobari/AutoSurf) by Amin Heyrani Nobari, Justin Rey, Suhas Kodali and Matthew Jones (MIT, 2023) [website](https://decode.mit.edu/projects/autosurf/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
260. [Approximating Full Conformal Prediction at Scale via Influence Functions](https://arxiv.org/pdf/2202.01315.pdf) by Javier Abad, Umang Bhatt, Adrian Weller, Giovanni Cherubin (Cambridge, Alan Turing Institute, ETH, Microsoft Research, 2023) [code](https://github.com/cambridge-mlg/acp) [video](https://www.youtube.com/watch?v=LRwm976poDE) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
261. [Robust Uncertainty Quantification using Conformalised Monte Carlo Prediction](https://arxiv.org/pdf/2308.09647.pdf) [Code](https://github.com/team-daniel/MC-CP) by Daniel Bethell, Simos Gerasimou, Radu Calinescu (University of York, 2023) [article](https://daniel-bethell.co.uk/posts/mccp) [code](https://github.com/team-daniel/MC-CP) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
262. [I do not know! but why?โ€โ€“ Local Model-Agnostic Example-based explanations of reject](https://www.sciencedirect.com/science/article/pii/S0925231223008457) [code](https://github.com/HammerLabML/LocalModelAgnosticExamplebasedExplanationsReject) by Andre Artelt, Roel Visser and Barbara Hammer (University of Bielefeld, 2023)
263. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://arxiv.org/pdf/2308.11975.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir and Ulf Johansson (KTH/Joenkoeping University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
264. [Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions](https://arxiv.org/pdf/2212.13629.pdf) by Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel (Princeton University, Columbia University, Harvard University, University of Toronto, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
265. [Improving Deep Learning-Based Defect Classification in Solar Cells using Conformal Prediction](https://backend.orbit.dtu.dk/ws/portalfiles/portal/333872349/FinalManuscript_2022_714_0602131208.pdf) by Vitus Bรธdker Thomsen, Claire Mantel, Gisele Benatto, Sรธren Forchhammer (DTU - Technical University of Denmark, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
266. [Adaptive Conformal Prediction by Reweighting Nonconformity Scores](https://arxiv.org/pdf/2303.12695.pdf) by Salim I. Amoukou and Nicolas J-B. Brunel and Nicolas J-B. Brunel (University Paris Saclay, Stellantis Paris, Quantmetry Paris, 2023) [code](https://github.com/salimamoukou/ACPI) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
267. [Copula-based conformal prediction for Multi-Target Regression](https://arxiv.org/abs/2101.12002) by Soundouss Messoudi, Sรฉbastien Destercke, Sylvain Rousseau (2021) [code](https://github.com/M-Soundouss/CopulaConformalMTR) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
268. [Conformal Meta-learners for Predictive Inference of Individual Treatment Effects](https://arxiv.org/abs/2308.14895) by Ahmed Alaa, Zaid Ahmad, and Mark van der Laan (2023) [code](https://github.com/AlaaLab/conformal-metalearners) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
269. [Adaptive conformal classification with noisy labels](https://arxiv.org/pdf/2309.05092.pdf) by Matteo Sesia, Y. X. Rachel Wangโ€ , Xin Tong [code](https://github.com/msesia/conformal-label-noise) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
270. [Heteroskedastic conformal regression](https://arxiv.org/pdf/2309.08313.pdf) by NICOLAS DEWOLF, BERNARD DE BAETS AND WILLEM WAEGEMAN (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (UGent, 2023)
271. [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://arxiv.org/pdf/2309.08603.pdf) by Rohan Sinha, Edward Schmerling, and Marco Pavone (Stanford, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
272. [Conformal Temporal Logic Planning using Large Language Models: Knowing When to Do What and When to Ask for Help](https://arxiv.org/abs/2309.10092) [project website](https://ltl-llm.github.io) by Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros (Washington University in St.Louis and University of Zurich, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
273. [Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method](https://www.nature.com/articles/s41598-022-17609-x) [code](https://github.com/unisb-bioinf/Conformal-Drug-Sensitivity-Prediction) by Kerstin Lenhof, Lea Eckhart, Nico Gerstner, Tim Kehl & Hans-Peter Lenhof (Saarland University, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
274. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candรจs (Stanford, 2019) [code](Conformalized Quantile Regression](https://github.com/yromano/cqr) [project](https://sites.google.com/view/cqr) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
275. [Fast nonlinear vector quantile regression](https://arxiv.org/pdf/2205.14977.pdf) by Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano and Alex M. Bronstein (Technion, 2023) [code](https://github.com/vistalab-technion/vqr) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
276. [Testing for Outliers with Conformal p-values](https://arxiv.org/abs/2104.08279) by Stephen Bates, Emmanuel Candes,Lihua Lei, Yaniv Romano,Matteo Sesia (Berkeley/Stanford/Technion, 2022) [code](https://github.com/msesia/conditional-conformal-pvalues) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
277. [Achieving Risk Control in Online Learning Settings](https://arxiv.org/abs/2205.09095) by Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano (Technion/Berkeley, 2021) [code](https://github.com/Shai128/rrc)
278. [Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ell2.12860) by Lorin Werthen-Brabants, Tom Dhaene, Dirk Deschrijver (Ghent University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
279. [Online NoVaS Conformal Volatility Prediction](https://proceedings.mlr.press/v204/canete23b/canete23b.pdf) [slides](https://copa-conference.com/presentations/3%20-%20COPA-2023.NoVas.v005.pptx) by Alejandro Canete (University of Chicago) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (2023)
280. [Market Implied Conformal Volatility Intervals](https://proceedings.mlr.press/v204/canete23a/canete23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023.ImpliedIntervals.v005.pptx) by Alejandro Canete (University of Chicago) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (2023)
281. [PUNCC: a Python Library for Predictive Uncertainty Calibration and Conformalization](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) [code](https://github.com/deel-ai/puncc) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
282. [The Venn-ABERS Testing for Change-Point Detection](https://proceedings.mlr.press/v204/nouretdinov23b/nouretdinov23b.pdf) [slides](https://copa-conference.com/presentations/Nouretdinov_poster_slides.pdf) by Ilia Nouretdinov and Alex Gammerman (Royal Holloway, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
283. [A Review of Nonconformity Measures for Conformal Prediction in Regression](https://proceedings.mlr.press/v204/kato23a/kato23a.pdf) [slides](https://copa-conference.com/presentations/COPA-2023%20-%20v2.pptx) by Yuko Kato, David M.J. Tax, Marco Loog (Delft University of Technology, Radboud University, Nijmegen, Netherlands, 2023)
284. [Approximating Score-based Explanation Techniques Using Conformal Regression](https://proceedings.mlr.press/v204/alkhatib23a/alkhatib23a.pdf) [slides](https://copa-conference.com/presentations/Amr-Alkhatib.pdf) by Amr Alkhatib, Henrik Bostroem, Sofiane Ennadir, Ulf Johansson (KTH, Joenkoeping University, 2023).
285. [Mondrian Predictive Systems for Censored Data](https://proceedings.mlr.press/v204/bostrom23a/bostrom23a.pdf) [slides](https://copa-conference.com/presentations/Bostrom.pdf) by Henrik Bostroem, Henrik Linusson, Anders Vesterberg (KTH, Ekkono Solutions AB, Scania CV AB, Sweden 2023).
286. [Evaluating Machine Translation Quality with Conformal Predictive Distributions](https://proceedings.mlr.press/v204/giovannotti23a/giovannotti23a.pdf) [slides](https://copa-conference.com/presentations/MTpres.pdf) by Patrizio Giovannotti (Royal Holloway, UK) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
287. [Confidence Calibration for Systems with Cascaded Predictive Modules](https://arxiv.org/pdf/2309.12510.pdf) by Yunye Gong, Yi Yao, Xiao Lin, Ajay Divakaran, Melinda Gervasio (SRI International, 2023)
288. [TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images](https://arxiv.org/pdf/2307.15638.pdf) by Benjamin Lambert, Florence Forbes, Senan Doyle, and Michel Dojat (Grenoble Institut Neurosciences / Univ. Grenoble Alpes, Pixyl, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
289. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
290. [Distribution-free risk assessment of regression-based machine learning algorithms](https://arxiv.org/pdf/2310.03545.pdf) by Sukrita Singh, Neeraj Sarna, Yuanyuan Li, Yang Lin, Agni Orfanoudaki (University of Oxford / MunichRe, 2023).
291. [CONFORMAL PREDICTION FOR DEEP CLASSIFIER VIA LABEL RANKING](https://arxiv.org/pdf/2310.06430.pdf) by Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei (Southern University of Science and Technology, ShanghaiTech University, Rutgers University, University of North Carolina at Chapel Hill, Chongqing University) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/ml-stat-Sustech/conformal_prediction_via_label_ranking)
292. [On the Expected Size of Conformal Prediction Sets](https://arxiv.org/abs/2306.07254) by Guneet S. Dhillon, George Deligiannidis, Tom Rainforth (University of Oxford, 2023) [code](https://github.com/Guneet-Dhillon/expected-conformal-prediction-set-size)๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
293. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
294. [Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification](https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Melki_Group-Conditional_Conformal_Prediction_via_Quantile_Regression_Calibration_for_Crop_and_ICCVW_2023_paper.html) by Paul Melki, Lionel Bombrun, Boubacar Diallo, Jรฉrรดme Dias, Jean-Pierre Da Costa by EXXACT Robotics (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
295. [Guaranteed Coverage Prediction Intervals with Gaussian Process Regression](https://arxiv.org/abs/2310.15641) by Harris Papadopoulos (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
296. [Conformal prediction with missing values](https://arxiv.org/abs/2306.02732) by Margaux Zaffran, Aymeric Dieuleveut, Julie Joss, Yaniv Romano (2023) [Video](https://icml.cc/virtual/2023/poster/23530) [summary](https://mzaffran.github.io/assets/files/Posters/cp_na_summary.pdf) [code](https://github.com/mzaffran/ConformalPredictionMissingValues) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
297. [TRIAGE: Characterizing and auditing training data for improved regression](https://arxiv.org/pdf/2310.18970.pdf) by Nabeel Seedat, Jonathan Crabbรฉ, Zhaozhi Qian, Mihaela van der Schaar (University of Cambridge, 2023) [code](https://github.com/seedatnabeel/TRIAGE)) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
298. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series)
299. [Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model](https://openreview.net/pdf?id=VeO03T59Sh) by Jiankai Sun, Jiankai_Sun, Yiqi Jiang, Jianing Qiu, Parth Talpur Nobel, Mykel Kochenderfer, Mac Schwager (Staford, Imperial College, 2023)
300. [CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift](https://openreview.net/pdf?id=GgdFLb94Ld) by Siddhartha Laghuvarapu, Zhen Lin, Jimeng Sun (University of Illinois Urbana-Champaign, 2023)
301. [PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction](https://openreview.net/forum?id=URrUpcp6Qh) Apoorva Sharma, Sushant Veer, Asher Hancock, Heng Yang, Marco Pavone, Anirudha Majumdar (NVIDIA Research, Harvard, Princeton, Stanford 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
302. [Conformal Prediction Sets for Ordinal Classification](https://openreview.net/pdf?id=YI4bn6aAmz) by Prasenjit Dey, Srujana Merugu, Sivaramakrishnan Kaveri (Amazon, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
303. [Beyond Confidence: Reliable Models Should Also Consider Atypicality](https://openreview.net/pdf?id=xDCmlkSavR) by Mert Yuksekgonul, Linjun Zhang, James Zou, Carlos Guestrin (Stanford/Rutgers) [code](https://github.com/mertyg/beyond-confidence-atypicality) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
304. [On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning](https://arxiv.org/abs/2311.12688) by Paul Scemama, Ariel Kapusta (The Mitre Corporation, 2023)
305. [A powerful rank-based correction to multiple testing under positive dependency](https://arxiv.org/pdf/2311.10900.pdf) by Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick (Bosch AI, 2023)
306. [Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models](https://arxiv.org/pdf/2311.13628.pdf) by Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel (Columbia University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
307. [Risk-Aware and Explainable Framework for Ensuring Guaranteed Coverage in Evolving Hardware Trojan Detection](https://arxiv.org/abs/2312.00009) by Rahul Vishwakarma, Amin Rezaei (California State University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
308. [Uncertainty quantification in automated valuation models with locally weighted conformal prediction](https://arxiv.org/abs/2312.06531) by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams (University of Oslo, North Carolina State University, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
309. [Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates](https://arxiv.org/abs/2312.07434) by Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann [code](https://github.com/nandantumu/conformal_region_designer) (University of Pennsylvania, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
310. [Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction](https://arxiv.org/abs/2312.08604) by Albert Lin, Somil Bansal (University of Southern California, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
311. [Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms](https://arxiv.org/abs/2311.11837) [code](https://github.com/NKI-AI/kandinsky-calibration) by Joren Brunekreef, Eric Marcus, Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen (The Netherlands Cancer Institute, University of Amsterdam) (2023)
312. [Forecasting CPI inflation under economic policy and geo-political uncertainties](https://arxiv.org/abs/2401.00249) by Shovon Sengupta, Tanujit Chakraborty, Sunny Kumar Singh (Fidelity Investments, Sorbonne University, BITS Pilani Hyderabad). (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[code](https://github.com/ctanujit/FEWNet)
313. [Conformal prediction of option prices](https://www.repository.utl.pt/handle/10400.5/29690) by Bastas Joao (University of Lisbon, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
314. [Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction](https://arxiv.org/abs/2401.01549) by Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, Mengdi Huai (Iowa University, Pennsylvania State University, Georgia Institute of Technology (2024).
315. [Greek Proverbs: Computational Spatial Attribution](https://www.researchsquare.com/article/rs-3360387/v2) by John Pavlopoulos and Panos Louridas, Athens University of Economics and Business, Greece (2024)
316. [Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations](https://arxiv.org/pdf/2401.01977.pdf) by Bingkai Wang, Fan Li and Mengxin (University of Michigan, Yale, University of Pennsylvania)
317. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
318. [Predicting Random Walks and a Data-Splitting Prediction Region](https://www.mdpi.com/2571-905X/7/1/2) by Mulubrhan G. Haile, Lingling Zhang, and David J. Olive (Westminster College, University of Albany, Southern Illinois University)
319. [Conformal Prediction via Regression-as-Classification](Conformal Prediction via Regression-as-Classification](https://openreview.net/forum?id=eKrYMGpXVY) by Etash Guha, Thomas Moellenhoff, Eugene Ndiaye, Shlok Natarajan (RIKEN Center for AI Project Tokyo, Japan, Salesforce, Apple, 2024)
320. [Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees](https://hal.science/hal-04389163/document) by Edgar Jaber, Vincent Blot, Nicolas Brunel, Vincent Chabridon, Emmanuel Remy, Bertrand Iooss, Didier Lucor, Mathilde Mougeot, Alessandro Leite (EDF R&D, Quantmetry, Paris-Saclay University, ENSIIE, Institut de Math ฬematiques de Toulouse, TAU, INRIA, 2024) [code](https://github.com/vincentblot28/conformalized_gp) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
321. [Integrating Uncertainty Awareness into Conformalized Quantile Regression](https://arxiv.org/abs/2306.08693) [code](https://github.com/rrross/UACQR) by Raphael Rossellini, Rina Foygel Barber and Rebecca Willett (University of Chicago, 2023) [code](https://github.com/rrross/uacqr) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
322. [Conformal Prediction Sets Improve Human Decision Making](https://arxiv.org/abs/2401.13744) by Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noel Vouitsis (Layer 6 AI, TD Securities, 2024)
323. [Benchmarking LLMs via Uncertainty Quantification](https://arxiv.org/abs/2401.12794) by Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu (Tencent AI Lab, University College London, University of Macau 2024).
324. [ACHO: Adaptive Conformal Hyperparameter Optimization](https://arxiv.org/abs/2207.03017) by Ricardo Doyle (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/rick12000/confopt)
325. [Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In-Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction](https://www.preprints.org/manuscript/202401.2041/v1) by John Waczak, Adam Aker, Lakitha O. H. Wijeratne, Shawhin Talebi, Bharana Fernando, Prabuddha Hathurusinghe, Mazhar Iqbal, David Schaefer, David J. Lary (University of Texas Dallas, 2024)
326. [Non-Exchangeable Conformal Language Generation with Nearest Neighbors](https://arxiv.org/abs/2402.00707) by Dennis Ulmer, Chrysoula Zerva, Andrรฉ F.T. Martins (IT University of Copenhagen, Instituto Superior Tรฉcnico, Universidade de Lisboa, 2024) [code](https://github.com/Kaleidophon/non-exchangeable-conformal-language-generation) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
327. [Group-Weighted Conformal Prediction](https://arxiv.org/abs/2401.17452) by Aabesh Bhattacharyya, Rina Foygel Barber (University of Chicago, 2024)
328. [Conformal Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects](https://arxiv.org/abs/2402.04906v1) by Jef Jonkers, Jarne Verhaeghe, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke (Ghent University, 2024) [code](https://github.com/valeman/cmc-learner)
329. [Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series](https://arxiv.org/abs/2402.05203) by Zitong Yang, Emmanuel Candรจs, Lihua Lei (Stanford, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/ZitongYang/bellman-conformal-inference)
330. [TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation)](https://www.nature.com/articles/s41592-024-02184-y.epdf) by James Zou, Eric Sun, Rong Ma, Anne Brunet, and Paloma Navarro Negredo (Stanford, Harvard, 2024) [code](https://github.com/sunericd/TISSUE)
331. [Conformal Predictive Programming for Chance Constrained Optimization](https://arxiv.org/pdf/2402.07407.pdf) by Yiqi Zhao, Xinyi Yu, Jyotirmoy V. Deshmukh and Lars Lindemann (University of Southern California. 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
332. [Introspective Planning: Guiding Language-Enabled Agents to Refine Their Own Uncertainty](https://arxiv.org/pdf/2402.06529.pdf) by Kaiqu Liang, Zixu Zhang, Jaime Fernandez Fisac(Princeton, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
333. [Regression Trees for Fast and Adaptive Prediction Intervals](https://arxiv.org/abs/2402.07357) by Luben M. C. Cabezasa, Mateus P. Ottoa, Rafael Izbicki, Rafael B. Sternc (Federal University of Sรฃo Carlos, University of Sรฃo Paulo,2024) [code](https://github.com/Monoxido45/clover) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
334. [Self-Consistent Conformal Prediction](https://arxiv.org/abs/2402.07307) by Lars van der Laan, Ahmed M. Alaa (University of Southern California, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
335. [Conformal Prediction via Regression-as-Classification](https://openreview.net/forum?id=eKrYMGpXVY) by Etash Guha, Shlok Natarajan, Thomas Mรถllenhoff, Mohammad Emtiyaz Khan, Eugene Ndiaye (RIKEN AIP, Salesforce, Apple, SambaNova Systems, 2023) [code](https://github.com/EtashGuha/R2CCP) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
336. [Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction](https://arxiv.org/abs/2402.15368) by Jun Wang, Guocheng He, and Yiannis Kantaros (Washington University in St Louis, 2024).
337. [Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks](https://arxiv.org/abs/2402.15406) by Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin (Purdue University, Florida State University, Yale, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
338. [API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access](https://arxiv.org/abs/2403.01216) by Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng (Zhejiang University, University of Illinois Urbana-Champaign Institute, University of Illinois Chicago, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
339. [Conformal Language Modeling](https://arxiv.org/abs/2306.10193)) by Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay (MIT, Berkeley, Google Research, UC San Francisco, 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
340. [A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach](https://arxiv.org/abs/2403.03215) by Skylar X. Wei, Lu Gan, Joel W. Burdick (California Institute of Technology,Georgia Institute of Technology, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
341. [Conformal prediction for multi-dimensional time series by ellipsoidal sets](https://arxiv.org/abs/2403.03850) by Chen Xu, Hanyang Jiang, and Yao Xie (Georgia Tech, 2024) [code](https://github.com/hamrel-cxu/MultiDimSPCI) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
342. [Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts](https://www.biorxiv.org/content/10.1101/2024.03.15.585269v1) by Saiveth Hernandez-Hernandez, Qianrong Guo, Pedro Ballester (Imperial College, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
343. [Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model](https://arxiv.org/abs/2403.15025) by Rui Xu, Yue Sun, Chao Chen, Parv Venkitasubramaniam, Sihong (The Hong Kong University of Science and Technology (Guangzhou), Lehigh University, Bethlehem, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
344. [Conformal link prediction for false discovery rate control](https://arxiv.org/abs/2306.14693) by Ariane Marandon (Sorbonne Universitรฉ, 2024)
345. [Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction](https://risk-calibrated-planning.github.io) by Justin Lidard, Ariel Bachman, Bryan Boateng, Anirudha Majumdar (Princeton, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
346. [Predictive Inference in Multi-environment Scenarios](https://arxiv.org/abs/2403.16336) by John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur (Stanford, Harvard, Amazon, 2024)
347. [Conformal online model aggregation](https://arxiv.org/abs/2403.15527) by Matteo Gasparin, Aaditya Ramdas (Carnegie Mellon University, University of Padova, 2024)
348. [Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition](https://arxiv.org/abs/2403.18973v1) by Floris den Hengst, Ralf Wolter, Patrick Altmeyer, Arda Kaygan (University of Amsterdam, ING, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
349. [Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data](https://arxiv.org/abs/2403.19950) by Xin Zou, Weiwei Liu (Wuhan University. 2024)
350. [Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets](https://arxiv.org/abs/2403.20149) by Yvet Renkema, Nico Brinkel & Tarek Alskaif (Wageningen University, Utrecht University, 2024). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
351. [FDR control and FDP bounds for conformal link prediction](https://arxiv.org/abs/2404.02542) by Gilles Blanchard, Guillermo Durand, Ariane Marandon-Carlhian, Romain Pรฉrier (University of Paris-Saclay, The Allan Turing Institute).
352. [Postprocessing of point predictions for probabilistic forecasting of electricity prices: Diversity matters](https://arxiv.org/abs/2404.02270) by Arkadiusz Lipiecki, Bartosz Uniejewski, Rafaล‚ Weron (Wrocล‚aw University of Science and Technology, 2024)
353. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/abs/1905.07886) by Christopher Katha, Florian Ziel (University Duisburg-Essen, 2020)
354. [On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices](https://arxiv.org/abs/2404.02722) by Alessandro Brusaferria, Andrea Ballarinoa, Luigi Grossib, Fabrizio Laurini (CNR, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Universoty of Parma 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
355. [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://arxiv.org/abs/1905.07886) by Christopher Katha, Florian Ziel (University Duisburg-Essen, 2020)
356. [A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests](https://www.sciencedirect.com/science/article/pii/S0306261924002836) by Jef Jonkers, Diego Nieves Avendano, Glenn Van Wallendael, Sofie Van Hoecke (Ghent University, 2024)
357. [CONFLARE: CONFormal LArge language model REtrieval](https://arxiv.org/abs/2404.04287) by Pouria Rouzrokh, Shahriar Faghani,
Cooper Gamble, Moein Shariatnia, Bradley J. Erickson (Mayo Clinic Artificial Intelligence Laboratory; Orthopedic Surgery Artificial Intelligence Laboratory, Mayo Clinic, MN, USA; Tehran University of Medical Sciences) [code](https://github.com/Mayo-Radiology-Informatics-Lab/conflare) [video](https://www.youtube.com/watch?v=I6zaX5UyHlY) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
358. [WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data](https://arxiv.org/abs/2404.05482) by Jintu Borah, Tanujit Chakraborty, Md. Shahrul Md. Nadzir, Mylene G. Cayetano, Shubhankar Majumdar (Sorbonne University, NIT Meghalaya, Universiti Kebangsaan, University of Philippines, 2024) [code](https://github.com/jborah2/WaveCatBoost-Time-Series-Model) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
359. [Pricing Catastrophe Bonds --- a Probabilistic Machine Learning Approach](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4789671) by Xiaowei Chen, Hong Li, Yufan Lu, Rui Zhou (Nankai University, University of Guelph, The University of Melbourne, 2024)
360. [Adaptive Conformal Prediction Intervals Using Data-Dependent Weights With Application to Seismic Response Prediction](https://ieeexplore.ieee.org/abstract/document/10497110) by Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (Florida Polytechnic University, University of Colorado Denver, University of Maryland, 2024)
361. [Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification](https://ieeexplore.ieee.org/abstract/document/10497903)
362. [Enhancing the reliability of probabilistic PV power forecasts using conformal prediction](https://www.sciencedirect.com/science/article/pii/S2667113124000093) by Yvet Renkema, Lennard Visser, Tarek AlSkaif (Wageningen University, Utrecht University, 2024)
363. [Random Projection Ensemble Conformal Prediction for High-Dimensional Classification](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794962) by Xiaoyu Qian, Jinru Wu, Ligong Wei, Youwu Lin )Guilin University of Electronic Technology, Guangxi Academy of Sciences, Guilin University of Electronic Technology, 2024)
364. [Conformal Predictive Systems Under Covariate Shift](https://arxiv.org/abs/2404.15018) by Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke (hent University, Belgium) [code](https://github.com/predict-idlab/crepes-weighted) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
365. [Metric-guided Image Reconstruction Bounds via Conformal Prediction](https://arxiv.org/abs/2404.15274) by Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok Veeraraghavan, Guha Balakrishnan (The University of Texas MD Anderson Cancer Cente, 2024)
366. [Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms](https://arxiv.org/abs/2404.13731) by Mehrdad Pournaderi, Yu Xiang (University of Utah, 2024)
367. [Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal Prediction](https://arxiv.org/abs/2404.15557) by Shili Sheng, Pian Yu, David Parker, Marta Kwiatkowska, Lu Feng (University of Oxford, 2024)
368. [Conformal Prediction of Motion Control Performance for an Automated Vehicle in Presence of Actuator Degradations and Failures](https://arxiv.org/abs/2404.16500) by Richard Schubert, Marvin Loba, Jasper Sรผnnemann, Torben Stolte, Markus Maurer (TU Braunschweig, 2024)
369. [Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction](https://arxiv.org/abs/2404.13002) by Paulo Henrique dos Santos, Valรฉria de Carvalho Santos, Eduardo Josรฉ da Silva Luz (Universidade Federal de Ouro Preto e Instituto Tecnolยดogico Vale, Universidade Federal de Ouro Preto, Brazil ๐Ÿ‡ง๐Ÿ‡ท, 2024)
370. [From Data Imputation to Data Cleaning โ€” Automated Cleaning of Tabular Data Improves Downstream Predictive Performance](https://proceedings.mlr.press/v238/jager24a.html) by Sebastian Jรคger, Felix Biessmann (Berlin University of Applied Sciences and Technology (BHT), Einstein Center Digital Future ๐Ÿ‡ฉ๐Ÿ‡ช, 2024) [code](https://github.com/se-jaeger/conformal-data-cleaning) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
371. [Online Calibrated and Conformal Prediction Improves Bayesian Optimization](https://proceedings.mlr.press/v238/deshpande24a.html) Shachi Deshpande, Charles Marx, Volodymyr Kuleshov (Cornell University, Stanford, ๐Ÿ‡บ๐Ÿ‡ธ, 2024)
372. [Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling](https://arxiv.org/abs/2401.08876) by Dongping Zhang, Angelos Chatzimparmpas, Negar Kamali, Jessica Hullman (Northwestern University, ๐Ÿ‡บ๐Ÿ‡ธ, 2024) [code](https://github.com/dpzhang/conformal-prediction-utility) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
373. [Adaptive Conformal Regression with Split-Jackknife+ Scores](https://openreview.net/forum?id=1fbTGC3BUD) by Nicolas Deutschmann, Mattia Rigotti, Marรญa Rodrรญguez Martรญnez (IBM Research, 2023)
374. [Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Proteinโ€“Ligand Predictions](https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.0c00476) by Lewis H. Mervin, Avid M. Afzal, Ola Engkvist, and Andreas Bender (2020)
375. [Conformal Prediction for Natural Language Processing: A Survey](https://arxiv.org/abs/2405.01976) by Margarida Campos, Antรณnio Farinhas, Chrisoula Zerva, Mario Figueiredo, Andre Martins (Instituto de Telecomunicaรงรตes, Instituto Superior Tรฉcnico, LUMLIS (Lisbon ELLIS Unit), Unbabel, Portugal, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
376. [Mitigating LLM Hallucinations via Conformal Abstention](https://arxiv.org/abs//2405.01563) by Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz, Andrรกs Gyรถrgy, Adam Fisch, Arnaud Doucet, Iuliya Beloshapka, Wei-Hung Weng, Yao-Yuan Yang, Csaba Szepesvรกri, Ali Taylan Cemgil, Nenad Tomasev (2024)
377. [CarbonCP: Carbon-Aware DNN Partitioning with Conformal Prediction for Sustainable Edge Intelligence](https://arxiv.org/abs/2404.16970) by Hongyu Ke, Wanxin Jin, Haoxin Wang (Georgia State University, Arizona State University, 2024)
378. [Conformalized Ordinal Classification with Marginal and Conditional Coverage](https://arxiv.org/abs/2404.16610) by Subhrasish Chakraborty, Chhavi Tyagi, Haiyan Qiao, Wenge Guo (New Jersey Institute of Technology, California State University San Bernardino, 2024)
379. [An Information Theoretic Perspective on Conformal Prediction](https://arxiv.org/abs/2405.02140) by Alvaro H.C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi (Qualcomm AI Research, 2024)
380. [Conformal Prediction with Learned Features](https://arxiv.org/pdf/2404.17487) by Shayan Kiyani, George Pappas, Hamed Hassani (University of Pennsylvania, 2024)
381. [Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction](https://arxiv.org/abs/2405.02634) by Protim Bhattacharjee, Peter Jung (Optical Sensor Systems, German Aerospace Center, 2024)
382. [A Conformal Prediction Score that is Robust to Label Noise](https://arxiv.org/abs/2405.02648) by Coby Penso, Jacob Goldberger (Bar-Ilan University, Israel, 2024)
383. [Building consumption anomaly detection: A comparative study of two probabilistic approaches](https://www.sciencedirect.com/science/article/pii/S0378778824003657) by Davor Stjelja, Vladimir Kuzmanovski, Risto Kosonen, Juha Jokisalo (Aalto University, Granlund Oy, Vaisala Oyj, Nanjing Tech University,2024)
384. [Robust Route Planning under Uncertain Pickup Requests for Last-mile Delivery](https://dl.acm.org/doi/pdf/10.1145/3589334.3645595) by Hua Yan, Heng Tan, Desheng Zhang, Haotian Wang, Yu Yang (Lehigh University, Rutgers University, USA; JD Logistics, Beijing, China)
385. [Improve robustness of machine learning via efficient optimization and conformal prediction](https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12173) by Yan Yan (Washington State University, 2024)
386. [Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration](https://arxiv.org/abs/2405.07374) by Shi-ang Qi, Yakun Yu, Russell Greiner (University of Alberta, 2024)
387. [Conformal Online Auction Design](https://arxiv.org/abs/2405.07038) by Jiale Han, Xiaowu Dai (University of California, 2024)
388. [Conformal Validity Guarantees Exist for Any Data Distribution](https://arxiv.org/abs/2405.06627) by Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria (John Hopkins University Genentech, 2024) [code](https://github.com/drewprinster/conformal-mfcs) ๐Ÿ’Ž๐Ÿ’Ž๐Ÿ’Ž๐Ÿ’Ž๐Ÿ’Ž๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
389. [Kernel-based optimally weighted conformal prediction intervals](https://arxiv.org/abs/2405.16828) by Jonghyeok Lee, Chen Xu, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
390. [Reliable anti-cancer drug sensitivity prediction and prioritization](https://www.nature.com/articles/s41598-024-62956-6) by Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, Andrea Volkamer & Hans-Peter Lenhof (Saarland University, 2024)
391. [Robust Conformal Prediction Using Privileged Information](https://arxiv.org/abs/2406.05405) by Shai Feldman, Yaniv Romano (2024)
392. [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://jmlr.org/papers/v25/22-1218.html) by Isaac Gibbs, Emmanuel J. Candรจs [code](https://github.com/isgibbs/DtACI) (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
393. [Adaptive Uncertainty Quantification for Trajectory Prediction Under Distributional Shift](https://arxiv.org/abs/2406.12100v1) by Huiqun Huang, Sihong He, Fei Miao (University of Connecticut, 2024)
394. [Conditional Shift-Robust Conformal Prediction for Graph Neural Network](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4862107) by Akansha Singh (Manipal Academy of Higher Education (MAHE) - Manipal Institute of Technology, 2024)
395. [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://jmlr.org/papers/v25/22-1218.html) by Isaac Gibbs, Emmanuel J. Candรจs (Stanford, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
396. [Conformal prediction for multi-dimensional time series by ellipsoidal sets](https://openreview.net/forum?id=uN39Tt9P8b) by Chen Xu, Hanyang Jiang, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
397. [Kernel-based optimally weighted conformal prediction intervals](https://arxiv.org/abs/2405.16828) Jonghyeok Lee, Chen Xu, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
398. [Transformer Conformal Prediction for Time Series](https://arxiv.org/abs/2406.05332) by Junghwan Lee, Chen Xu, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/Jayaos/TCPTS)
399. [Identifying Homogeneous and Interpretable Groups for Conformal Prediction](https://openreview.net/pdf?id=5qW3Ojxt9m) by Natalia Martinez, Dhaval C Patel, Chandra Reddy, Giridhar Ganapavarapu, Roman Vaculin, Jayant Kalagnanam (IBM Research, 2024)
400. [Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing](https://openreview.net/pdf?id=wu3JIjKmXQ) by Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Gรผnnemann (Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany; Volkswagen Group Innovation; Technical Univ. of Munich, 2024)
401. [Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions](Michelle Zhao, Reid Simmons, Henny Admoni, Andrea Bajcsy) by Michelle Zhao, Reid Simmons, Henny Admoni, Andrea Bajcsy (Robotics Institute, Carnegie Mellon University, 2024)
402. [Conformal Load Prediction with Transductive Graph Autoencoders](https://arxiv.org/abs/2406.08281) by Rui Luo, Nicolo Colombo (City University of Hong Kong; Royal Holloway, University of London, 2024) [code](https://github.com/luo-lorry/conformal-load-forecasting)
403. [Conformal Recursive Feature Elimination](https://arxiv.org/abs/2405.19429) by Marcos Lopez-De-Castro, Alberto Garcฤฑa-Galindo, Ruben Armananzas (Universidad de Navarra, 2024)
404. [Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference](https://arxiv.org/abs/2406.14904) Carlos Sebastiana,Carlos E. Gonzalez-Guillenc, Jesus Juane (Fortia Energฤฑa, Universidad Politecnica de Madrid, Instituto de Ciencias Matematicas, Madrid) [code](https://github.com/CCaribe9/HQR-WACI) (2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
405. [Conformal time series decomposition with component-wise exchangeability](https://arxiv.org/abs/2406.16766) by Derck W. E. Prinzhorn, Thijmen Nijdam, Putri A. van der Linden, Alexander Timans (University of Amsterdam, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/dweprinz/CP-TSD)
406. [Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction](https://arxiv.org/abs/2402.15368) Jun Wang, Guocheng He, Yiannis Kantaros (Washington University in St Louis, 2024)
407. [Online Calibrated and Conformal Prediction Improves Bayesian Optimization](https://arxiv.org/abs/2112.04620) by Shachi Deshpande, Charles Marx, Volodymyr Kuleshov (Cornell Tech and Cornell University, Stanford, 2024)
408. [An Information Theoretic Perspective on Conformal Prediction](https://arxiv.org/pdf/2405.02140) by Alvaro H.C. Correia Fabio Valerio Massoli Christos Louizosโ€  Arash Behboodi (Qualcomm AI Research, 2024)
409. [Conformal Prediction for Natural Language Processing: A Survey](https://arxiv.org/abs/2405.01976) by Margarida M. Campos, Antรณnio Farinhas, Chrysoula Zerva, Mรกrio A.T. Figueiredo, Andrรฉ F.T. Martins (Instituto de Telecomunicaรงรตes, Instituto Superior Tรฉcnico, LUMLIS (Lisbon ELLIS Unit), Unbabel, 2024)
410. [Conformal Prediction for Causal Effects of Continuous Treatments](https://arxiv.org/abs/2407.03094) by Maresa Schrรถder, Dennis Frauen, Jonas Schweisthal, Konstantin HeรŸ, Valentyn Melnychuk, Stefan Feuerriegel (LMU Munich, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
411. [Uncertainty-Aware Decarbonization for Datacenters](https://arxiv.org/abs/2407.02390) by Amy Li, Sihang Liu, Yi Ding (Uiversity of Waterloo, 2024)
412. [Conditionally valid Probabilistic Conformal Prediction](https://arxiv.org/abs/2407.01794) by Vincent Plassier, Alexander Fishkov, Maxim Panov, Eric Moulines (Lagrange Mathematics and Computing Research Center, Mohamed bin Zayed University of Artificial Intelligence, CMAP, Ecole Polytechnique, Skolkovo Institute of Science and Technology (2024)
413. [Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I.](https://arxiv.org/abs/2407.02464) by Harrie Oosterhuis,Zhen Qin, Xuanhui Wang, Rolf Jagerman, Michael Bendersky, (Radbound University, Google Research, 2024)
414. [Trustworthy Classification through Rank-Based Conformal Prediction Sets](https://arxiv.org/abs/2407.04407) by Rui Luo, Zhixin Zhou (City University of Hong Kong, 2024)
415. [Distributionally robust risk evaluation with an isotonic constraint](https://arxiv.org/abs/2407.06867) by Yu Gui, Rina Foygel Barber, Cong Ma (University of Chicago, 2024) [code](https://github.com/yugjerry/iso-DRL)
416. [Split Conformal Prediction under Data Contamination](https://arxiv.org/abs/2407.07700) by Jase Clarkson, Wenkai Xu, Mihai Cucuringu, Gesine Reinert [code](https://github.com/jase-clarkson/cp_under_data_contamination)
417. [Weighted Aggregation of Conformity Scores for Classification](https://arxiv.org/abs/2407.10230) by Rui Luo, Zhixin Zhou (2024)
418. [Meta-Analysis with Untrusted Data](https://arxiv.org/abs/2407.10230) by Shiva Kaul, Geoffrey J. Gordon (Carnegie Mellon, 2024)
419. [Robust Yet Efficient Conformal Prediction Sets](https://arxiv.org/abs/2407.09165) by Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski (University of Cologne, 2024)
420. [Learning Cellular Network Connection Quality with Conformal](https://arxiv.org/abs/2407.10976) by Hanyang Jiang, Elizabeth Belding, Ellen Zegure, Yao Xie (Universiry of Georgia, 2024)
421. [Conformal Thresholded Intervals for Efficient Regression](https://arxiv.org/abs/2407.14495) by Rui Luo, Zhixin Zhou (City University of Hong Kong, 2024)
422. [Conformal Predictions under Markovian Data](https://arxiv.org/abs/2407.15277) by Frรฉdรฉric Zheng, Alexandre Proutiere (KTH, 2024)
423. [Entropy Reweighted Conformal Classification](https://arxiv.org/abs/2407.17377) by Rui Luo, Nicolo Colombo (City University of Hong Kong, Hong Kong, Royal Holloway, University of London). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
424. [SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning](https://arxiv.org/abs/2407.17460) by Jianpeng Yao, Xiaopan Zhang, Yu Xia, Zejin Wang, Amit K. Roy-Chowdhury, Jiachen Li (University of California, Riverside) [project](https://sonic-social-nav.github.io)
425. [Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)](https://arxiv.org/abs/2405.06627) by Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria (John Hopkins University, Genentech, 2024) [video](https://www.youtube.com/watch?v=HMXZnPmgbxY)
426. [Robust Conformal Volume Estimation in 3D Medical Images](https://arxiv.org/abs/2407.19938) by Benjamin Lambert, Florence Forbes, Senan Doyle, Michel Dojat (University Grenoble Alps, 2024)
427. [A CONFORMALIZED LEARNING OF A PREDICTION SET WITH APPLICATIONS TO MEDICAL IMAGING CLASSIFICATION](https://arxiv.org/abs/2408.05037) by Roy Hirsch, Jacob Goldberger (Bar-Ilan University, Ramat-Gan, Israel, 2024)
428. [Quantifying uncertainty in climate projections with conformal ensembles](https://arxiv.org/pdf/2408.06642) by Trevor Harris, Ryan Sriver (Texas A&M University, University of Illinois at Urbana Champaign, 2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
429. [CONFORMALIZED INTERVAL ARITHMETIC WITH SYMMETRIC CALIBRATION](https://arxiv.org/abs/2408.10939) by Rui Luo and Zhixin Zhoi (City University of Hong Kong, 2024) [code](https://github.com/luo-lorry/CIA) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
430. [Split Conformal Prediction and Non-Exchangeable Data](https://www.jmlr.org/papers/v25/23-1553.html) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, Joรฃo Vitor Romano (IMPA, Brazil, 2024) [code](https://github.com/jv-rv/split-conformal-nonexchangeable)
431. [PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification](https://arxiv.org/pdf/2408.15458) by Alek Frohlic, Thiago Ramos, Gustavo Cabello, Isabela Buzatto, Rafael Izbicki, Daniel
Tiezzi (UFSC, Florianopolis, UFSCar, Sao Carlos, USP, Ribeirao Preto, Brazil) (2024)
432. [Conformal Prediction in Dynamic Biological Systems](https://arxiv.org/abs/2409.02644) by Alberto Portela, Julio R. Banga, Marcos Matabuena (Spanish National Research Council, Harvard University) (2024)
433. [Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification](https://arxiv.org/abs/2409.01236) by Kangdao Liu, Tianhao Sun, Hao Zeng, Yongshan Zhang, Chi-Man Pun, Chi-Man Vong (University of Macau, Southern University of Science and Technology, China University of Geosciences, 2024)
434. [Formal Verification and Control with Conformal Prediction](https://arxiv.org/abs/2409.00536) by Lars Lindemann, Yiqi Zhao, Xinyi Yu, George J. Pappas, Jyotirmoy V. Deshmukh (University of Southern California, University of Pennsylvania) (2024)
435. [Segmentation uncertainty with statistical guarantees in prostate MRI](https://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0001636.pdf) Kevin Mekhaphan Nguyen, Alvaro Fernandez-Quilez, University of Stavanger, Norway. Stavanger University Hospital, Stavanger, Norway (2024)
436. [Making Deep Learning Models Clinically Useful - Improving Diagnostic Confidence in Inherited Retinal Disease with Conformal Prediction](https://openreview.net/pdf?id=eQ0Z2vrERs) by Biraja Ghoshal, William Woof, Bernardo Mendes et al (University College London Institute of Ophthalmology, Moorfields Eye Hospital, Oxford Eye Hospital, Oxford, United Kingdom, Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Japan, St Paulโ€™s Eye Unit, Liverpool University Hospitals NHS Foundation Trust,Liverpool, United Kingdom, Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina, Federal University of Sao Paulo, Sรฃo Paulo, SP, Brazil, Department of Ophthalmology, University Hospital Bonn,
Rheinische-Friedrich-Wilhelms Universitรคt Bonn, Germany, 2024). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
437. [Measuring the Confidence of Single-Point Traffic Forecasting Models: Techniques, Experimental Comparison, and Guidelines Toward Their Actionability](https://ieeexplore.ieee.org/ielx7/6979/4358928/10472567.pdf) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
438. [Conformal Multilayer Perceptron-Based Probabilistic Net-Load Forecasting for Low-Voltage Distribution Systems with Photovoltaic Generation](https://www.alspereira.info/wp-content/uploads/2024/09/SmartGridComm-2024.pdf) by Anthony Faustine and Lucas Pereira, Center for Intelligent Power (CIP), Eaton Corporation, Dublin, Ireland; โ€ ITI, LARSyS, Tecnico Lisboa, Lisbon, Portugal (2024)
439. [Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference](https://arxiv.org/abs/2408.01582) by Hengrui Cai, Huaqing Jin, Lexin Li (University of California Irvine, University of California San Francisco, University of California Berkeley, 2024).
440. [Quantifying Aleatoric and Epistemic Dynamics Uncertainty via Local Conformal Calibration](https://arxiv.org/abs/2409.08249) by Luรญs Marques, Dmitry Berenson (University of Michigan, Ann Arbour, USA) [project](https://um-arm-lab.github.io/lucca/)
441. On the uncertainty of real estate price predictions](https://rem.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_0314_2024.pdf) by Joรฃo A. Bastos, Jeanne Paquette (University of Lisbon, Portugal, 2024)
442. [Adaptive Uncertainty Quantification for Generative AI](https://arxiv.org/abs/2408.08990) by Jungeum Kim, Sean O'Hagan, Veronika Rockova (University of Chicago, 2024) [code](https://github.com/JungeumKim/conformal_tree?tab=readme-ov-file)
443. [Localized Conformal Prediction: A Generalized Inference Framework to Conformal Prediction](https://arxiv.org/abs/2106.08460) by Leying Guan (Yale University, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
444. [Split Localized Conformal Prediction](https://arxiv.org/pdf/2206.13092.pdf) by Xing Han Ziyang Tang Joydeep Ghosh Qiang Liu
(University of Texas at Austin, 2022) [code](https://github.com/aaronhan223/SLCP)
445. [Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection](https://proceedings.mlr.press/v238/han24b/han24b.pdf) by Yujin Han, Mingwenchan Xu, Leying Guan (Universtity of Hong Kong, Northwestern University, Yale University, 2024) [code](https://github.com/yujinhanml/CSForest)
446. [Length Optimization in Conformal Prediction](https://arxiv.org/abs/2406.18814) by Shayan Kiyani, George Pappas, Hamed Hassani (University of Pennsylvania,2024) [code](https://github.com/shayankiyani98/CP/blob/main/CPL.ipynb) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## 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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [Adaptive Conformal Predictions for Time Series](https://arxiv.org/abs/2202.07282) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ฬron, Yannig Goude, and Julie Josse (2022) [Python Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [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 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) [Code Python](https://github.com/TaeseongYoon/DCP) [code R](https://github.com/kwuthrich/Replication_DCP)
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
13. [A General Framework For Multi-step Ahead Adaptive Conformal Heteroscedastic Time Series Forecasting](https://www.sciencedirect.com/science/article/pii/S0925231224012050/pdfft?md5=2f884ccf3a69148127232428fbf2042a&pid=1-s2.0-S0925231224012050-main.pdf) by Martim Sousa, Ana Maria Tomรฉ, University of Aveiro (2022) [Code](https://github.com/Quilograma/AdaptiveEnbMIMOCQR) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) by Sophia Sun, Rose Yu (University of California, San Diego, 2022) [code](https://github.com/Rose-STL-Lab/CopulaCPTS) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
19. [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)
20. [Amazon Fortuna](https://aws-fortuna.readthedocs.io/en/latest/examples/enbpi_ts_regression.html)
21. [Improved Online Conformal Prediction via Strongly Adaptive Online Learning](https://arxiv.org/abs/2302.07869) by Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai (2023) [code](https://github.com/salesforce/online_conformal) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
22. [Conformal Prediction for Time Series with Modern Hopfield Networks](https://arxiv.org/pdf/2303.12783.pdf) by Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter (Johannes Kepler University, Linz, 2023) [code]([https://github.com/ml-jku/HopCPT](https://github.com/ml-jku/HopCPT)) [blogpost](https://ml-jku.github.io/HopCPT/) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
23. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/abs/2212.03463) by Chen Xu, Yao Xie (Georgia Tech) [code](https://github.com/hamrel-cxu/SPCI-code/tree/main) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
24. [Conformal Predictions for longitudinal data](https://arxiv.org/pdf/2310.02863.pdf) by Devesh Batra, Salvatore Mercuri and Raad Khraishi (Data Science & Innovation - NatWest Group, Institute of Finance and Technology, UCL, UK, 2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
25. [Conformal PID Control for Time Series Prediction](https://arxiv.org/abs/2307.16895) by Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani (Berkeley/Stanford, NeurIPS2023) [code](https://github.com/aangelopoulos/conformal-time-series) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
26. [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://arxiv.org/abs/2401.04612) by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb (Universidade Federal de Sao Carlos, Brazil and University of Mons, Belgium, 2024) [code](https://github.com/valeman/conf_tpp) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
27. [Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series](https://arxiv.org/abs/2402.05203) by Zitong Yang, Emmanuel Candรจs, Lihua Lei (Stanford, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ[code](https://github.com/ZitongYang/bellman-conformal-inference)
28. [Conformal prediction for multi-dimensional time series by ellipsoidal sets](https://arxiv.org/abs/2403.03850) by Chen Xu, Hanyang Jiang, and Yao Xie (Georgia Tech, 2024) [code](https://github.com/hamrel-cxu/MultiDimSPCI) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
29. [Conformalized predictive simulations for univariate time series](https://www.researchgate.net/publication/379643443_Conformalized_predictive_simulations_for_univariate_time_series) by Thierry Moudiki (2024)
30. [Kernel-based optimally weighted conformal prediction intervals](https://arxiv.org/abs/2405.16828) by Jonghyeok Lee, Chen Xu, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
31. [Transformer Conformal Prediction for Time Series](https://arxiv.org/abs/2406.05332) by Jonghyeok Lee, Chen Xu, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/Jayaos/TCPTS)
32. [Conformal prediction for multi-dimensional time series by ellipsoidal sets](https://openreview.net/forum?id=uN39Tt9P8b) by Chen Xu, Hanyang Jiang, Yao Xie (Georgia Tech, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
33. [Conformal time series decomposition with component-wise exchangeability](https://arxiv.org/abs/2406.16766) by Derck W. E. Prinzhorn, Thijmen Nijdam, Putri A. van der Linden, Alexander Timans (University of Amsterdam, 2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [code](https://github.com/dweprinz/CP-TSD)
34. [Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference](https://arxiv.org/abs/2406.14904) Carlos Sebastiana,Carlos E. Gonzalez-Guillenc, Jesus Juane (Fortia Energฤฑa, Universidad Politecnica de Madrid, Instituto de Ciencias Matematicas, Madrid) [code](https://github.com/CCaribe9/HQR-WACI) (2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
35. [JANET: Joint Adaptive predictioN-region Estimation for Time-series](https://arxiv.org/abs/2407.06390) by Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert (2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
36. [Split Conformal Prediction and Non-Exchangeable Data](https://www.jmlr.org/papers/v25/23-1553.html) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, Joรฃo Vitor Romano (IMPA, Brazil, 2024) [code](https://github.com/jv-rv/split-conformal-nonexchangeable)
431. [PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification]
432. [Unsupervised radiometric change detection from synthetic aperture radar images](https://telecom-paris.hal.science/hal-04683910/)
Thomas Bultingaire , Inรจs Meraoumia, Christophe Kervazo, Loรฏc Denis, Florence Tupin (LTCI, Telยด ecom Paris, Institut Polytechnique de Paris, Universite Jean Monnet Saint-Etienne, CNRS, Institut dโ€™Optique Graduate School, Laboratoire Hubert Curien, France 2024)
433. [Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints](https://arxiv.org/abs/2409.07902) by Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Petar Popovski, Osvaldo Simeone (King's College London, UK, Aaborg University, Denmark).

## Presentation Slides
1. [Machine Learning for Probabilistic Prediction, Seattle Artificial Intelligence Workshops Meetup](https://www.researchgate.net/publication/371566526_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin, 2023 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [Machine Learning for Probabilistic Prediction](https://github.com/valeman/awesome-conformal-prediction/blob/main/Machine%20Learning%20for%20Probabilistic%20Prediction.pdf) by Valery Manokhin, 2022 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
3. [Adaptive Conformal Anomaly Detection for Time-series](https://cml.rhul.ac.uk/copa2017/presentations/burnaev.pdf) by Evgeny Burnaev, Alexander Bernstein, Vlad Ishimtsev and Ivan Nazarov (Skoltech, Moscow, Russia, 2017)
4. [Nonparametric predictive distributions based on conformal prediction](https://cml.rhul.ac.uk/copa2017/presentations/vovk.pdf) by Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie, Ilia Nouretdinov and Alex Gammerman (Royal Holloway, University of London Rutgers University, 2017)
5. [What Can Conformal Inference Offer to Statistics?](https://lihualei71.github.io/Job_Talk_Lihua_Lei.pdf) by Lihua Lei, Stanford University
6. [Conformal Regressors and Predictive Systems โ€“ a Gentle Introduction](https://copa-conference.com/presentations/COPA_2022_Presentation__Conformal_Regressors_and_Predictive_Systems___a_Gentle_Introduction.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
7. [Applications of Conformal Predictors](https://copa-conference.com/presentations/lars_ernst.pdf) by Ernst Ahlberg and Lars Carlsson (Stena Line, 2022)
8. [crepes: a Python Package for Conformal Regressors and Predictive Systems](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
9. [Assessing Explanation Quality by Venn Prediction](https://copa-conference.com/presentations/amr.pdf) by Amr Alkhatib, Henrik Bostrรถm and Ulf Johansson (2022)
10. [Well-Calibrated Rule Extractors](https://copa-conference.com/presentations/Johansson_U_COPA2022_pres.pdf) by Ulf Johansson, Tuwe Lรถfstrรถm, Niclas Stรฅhl (2022)
11. [Calibration of Natural Language Understanding Models with Venn-ABERS Predictors](Calibration of Natural Language Understanding Models with Venn-ABERS Predictors](https://copa-conference.com/presentations/patrizio.pdf) by Patrizio Giovannotti (2022)
12. [Reinforcement Learning Prediction Intervals with Guaranteed Fidelity](https://web.engr.oregonstate.edu/~tgd/talks/dietterich-caml-rl-prediction-intervals.pdf) by Thomas Dietterich (University of Oregon, 2022)
13. [Conformal Prediction beyond exchangeability](https://copa-conference.com/presentations/conformal_UAI_tutorial_2.pdf) by Rina Foygel Barber (University of Chicago, 2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
14. [Split conformal prediction for dependant data](http://www.im.ufrj.br/~coloquiomea/apresentacoes/orenstein_2022.pdf) by Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos and Joรฃo Vitor Romano (2022)
15. [Conformal prediction of small-molecule drug resistance in cancer cell lines](https://copa-conference.com/presentations/saiveth.pdf) by Saiveth Hernandez-Hernandez, Sachin Vishwakarma and Pedro Ballester
16. [Sequential Predictive Conformal Inference for Time Series](https://arxiv.org/pdf/2212.03463.pdf) by Chen Xu, Yao Xie (Georgia Tech, 2022) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
17. [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://arxiv.org/abs/2212.03281) by Sophia Sun, Rose Yu (University of California, San Diego, 2022)
18. [Uncertainty estimation in NLP](https://sites.google.com/view/uncertainty-nlp#h.is5zc8lcnuki) by Tal Schuster, Adam Fisch (MIT, 2022)
19. [Conformal Prediction: an Introduction](https://github.com/online-ml/river/files/10098389/Conformal_Prediction_Presentation.pdf) by Leo Andeol (2022)
20. [Adaptive Conformal Predictions for Time Series](https://github.com/valeman/awesome-conformal-prediction/blob/main/Adaptive%20Conformal%20Predictions%20for%20Time%20Series.pdf) by Margaux Zaffran (ICML,2021)
21. [EnbPI poster](https://github.com/valeman/awesome-conformal-prediction/blob/main/EnbPI_poster.pdf) by Chen Xu, Yao Xie (2021)
22. [Machine Learning for Probabilistic Prediction](https://www.researchgate.net/publication/370012303_Machine_Learning_for_Probabilistic_Prediction) by Valery Manokhin (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
23. [Conformal Inference with Tidymodels](https://topepo.github.io/posit-2023-conformal/#/title-slide) by Max Kuhn (posit conference 2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
24. [Graceful handling of large imbalanced datasets using Conformal Prediction](https://bigchem.eu/sites/default/files/AZ_school_Norinder_public.pdf) by Ulf Norinder and Fredrik Svensson ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
25. [Conformalized Quantile Regression](https://arxiv.org/abs/1905.03222) by Yaniv Romano, Evan Patterson, Emmanuel J. Candรจs (Stanford, 2019)
26. [Leveraging Conformal Prediction for Calibrated Probabilistic Time Series Forecasts to Accelerate the Renewable Energy Transition](https://speakerdeck.com/ingevandenende/leveraging-conformal-prediction-for-calibrated-probabilistic-time-series-forecasts-to-accelerate-the-renewable-energy-transition) by Inge van den Ende (Dexter Energy, 2023). TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## 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 Candรจs](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/), Technionโ€”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 Bostrรถm](https://www.kth.se/profile/henbos), KTH, Sweden
12. [Ulf Johansson](https://scholar.google.com/citations?user=OZjCgIsAAAAJ&hl=en), Jรถnkรถping University, Sweden
13. [Henrik Linusson](https://scholar.google.se/citations?user=Xl8W39cAAAAJ&hl=en), University of Borรฅs, Sweden
14. [Harris Papadopoulos](http://staff.fit.ac.cy/com.ph/), Frederick University, Cyprus
15. [Vladimir V'yugin](http://iitp.ru/ru/users/125.htm), Institute for Information Transmission Problems (IITP), Russia
16. [Evgeny Burnaev](https://faculty.skoltech.ru/people/evgenyburnaev), Skoltech, Russia
17. [Aaditya Ramdas](http://stat.cmu.edu/~aramdas/), Carnegie Mellon, USA
18. [Benjamin LeRoy](https://benjaminleroy.github.io), Carnegie Mellon, USA
19. [Victor Chernozhukov](https://www.mit.edu/~vchern/), MIT, USA
20. [Ulf Norinder](https://scholar.google.com/citations?user=i5hUEFwAAAAJ&hl=en), Stockholm University, Sweden
21. [Ola Spjuth](https://pharmb.io), Uppsala University, Sweden
22. [Ilia Nouretdinov](https://cml.rhul.ac.uk/people/nouretdinov/index.htm), Royal Holloway, United Kingdom
23. [Matteo Fontana](https://scholar.google.com/citations?user=U7jODH8AAAAJ&hl=it), Royal Holloway, University of London, United Kingdom
24. [Yao Xie](https://www2.isye.gatech.edu/~yxie77/), Georgia Institute of Technology
25. [Zhimeo Ren](https://zhimeir.github.io), University of Chicago
26. [Rafael Izbicki](http://www.rizbicki.ufscar.br), Federal University of Sรฃo Carlos (UFSCar) Brazil
27. [Rina Foygel Barber](https://rinafb.github.io) University of Chicago
28. [Matteo Sesia](https://msesia.github.io) University of Southern California, Marshall School of Business
29. [Simone Vantini](https://scholar.google.it/citations?user=Cy2mv3YAAAAJ&hl=it) MOX - Department of Mathematics, Politecnico di Milano

## Articles
1. [Measuring Models' Uncertainty: Conformal Prediction](https://blog.dataiku.com/measuring-models-uncertainty-conformal-prediction) by
Leo Dreyfus-Schmidt (Dataiku, 2020). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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. [โ€œMAPIEโ€ 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). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
11. [How to Detect Anomalies โ€” 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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
15. [How to predict quantiles in a more intelligent way (or โ€˜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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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, NeurIPSโ€™22 paper) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
27. [Putting clear bounds on uncertainty](https://news.mit.edu/2023/putting-clear-bounds-uncertainty-0123) (MIT, 2023)
28. [Conformal prediction theory explained](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-theory-explained-14a35226df80) by Artem Ryasik (2023)
29. [Easy Distribution-Free Conformal Intervals for Time Series](https://towardsdatascience.com/easy-distribution-free-conformal-intervals-for-time-series-665137e4d907) by Michael Keith (2023)
30. [Another (Conformal) Way to Predict Probability Distributions](https://towardsdatascience.com/another-conformal-way-to-predict-probability-distributions-fcc63e78680d) by Harrison Hoffman (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
31. [How to use full (transductive) Conformal Prediction])(https://valeman.medium.com/how-to-use-full-transductive-conformal-prediction-7ed54dc6b72b) by Valery Manokhin (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
32. [Conformal Prediction for Regression](https://medium.com/@artem_ryasik/conformal-prediction-for-regression-1ba24f1442df) (using KNIME) by Artem Ryasik (2023)
33. [Dynamic Conformal Intervals for any Time Series Model](https://towardsdatascience.com/dynamic-conformal-intervals-for-any-time-series-model-d1638aa48527) by Michael Keith (2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
34. [Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting](https://towardsdatascience.com/hitting-time-forecasting-the-other-way-for-time-series-probabilistic-forecasting-6c3b6496c353) by Marco Cerliani (2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
35. [Series of Medium articles about Conformal Prediciton (in Portuguese)](https://github.com/gusbruschi13/Conformal-Prediction) by Gustavo Bruschi (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
36. [Stanford statisticians and Washington Post data scientists build more honest prediction models](https://news.stanford.edu/2021/03/19/honesty-statistical-models/) [Code](https://github.com/washingtonpost/elex-live-model) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
37. [Jackknife+ โ€” a Swiss knife of Conformal Prediction for regression](https://medium.com/@valeman/jackknife-a-swiss-knife-of-conformal-prediction-for-regression-ce3b56432f4f) by Valeriy Manokhin (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
38. [Clinical AI tools must convey predictive uncertainty for each individual patient](https://www.nature.com/articles/s41591-023-02562-7) (2023)๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
39. [How to use Conformal Prediction](https://kiel.ai/conformal-prediction/) by Yannick Kรคlber (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
40. [Model Diagnostics: Prediction Uncertainty](https://piml.medium.com/model-diagnostics-prediction-uncertainty-990ff0bd2c4e) by PiML team, Wells Fargo (2023).
41. [Make any predictor uncertainty-aware via conformal prediction](https://mlwithouttears.com/2023/11/15/make-any-predictor-uncertainty-aware-via-conformal-prediction/) by Lorenzo Maggi (2023)
42. [Conformal predictive systems - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-predictive-systems-895c40bac2ca) by Artem Ryasik (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
43. [Conformal prediction for classification - A hands-on codeless example with KNIME](https://medium.com/low-code-for-advanced-data-science/conformal-prediction-for-classification-6bf152abb491) by Artem Ryasik (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
44. [Conformalized quantile regression](https://mlwithouttears.com/2024/01/17/conformalized-quantile-regression/) by Lorenzo Maggi (2024)
45. [Leveraging conformal prediction in Python to accelerate the renewable energy transition](https://medium.com/@icvandenende/leveraging-conformal-prediction-in-python-to-accelerate-the-renewable-energy-transition-09b5c855f69d) by Inge van den Ende (2024)
46. [Fifty (four, actually) shades of conformal prediction](https://mlwithouttears.com/2024/02/04/fifty-four-actually-shades-of-conformal-prediction/) by Lorenzo Maggi (2024)
47. [Use case adapted prediction intervals by means of conformal predictions and a custom non conformity score](https://medium.com/@arnaud.gc.capitaine/use-case-adapted-prediction-intervals-by-means-of-conformal-predictions-and-a-custom-non-conformity-b4fb28d2a4f7) by Arnaud Capitaine (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
48. [Prediction Intervals using Conformalized Quantile Regression](https://vincentwtrs.github.io/2024-03-06-conformal_prediction_prediction_intervals/) by Vincent Wauters (2024).
49. [Conformalized Quantile Regression for Time Series Probabilistic Forecasting](https://dataman-ai.medium.com/conformalized-quantile-regression-for-time-series-probabilistic-forecasting-85a2a1047119) by Chris Kuo (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
50. [Uncertainty Quantification and Why You Should Care](https://towardsdatascience.com/uncertainty-quantification-and-why-you-should-care-3f8a651f1956) by Jonte Dancker (2024)
51. [Probabilistic forecasting I: Temperature](https://stephane-degeye.medium.com/probabilistic-forecasting-i-temperature-f96ded1e7247) by Stephane Degeye (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
52. [Predict CO2 emissions by vehicles with Conformal Prediction](https://medium.com/@c.giancaterino/predict-co2-emissions-by-vehicles-with-conformal-prediction-3d40a5e8c136) by Claudio Giorgio Giancaterino (2024)

## 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/) by Valeriy Manokhin (2022)
3. [Regression prediction intervals with MAPIE](https://www.kaggle.com/code/carlmcbrideellis/regression-prediction-intervals-with-mapie) by Carl McBride Ellis (2022)
4. [Lgbm & Mapie & birth weight, oh my!](https://www.kaggle.com/code/paddykb/lgbm-mapie-birth-weight-oh-my) by Patrick Blackwill (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [Classifier calibration using Venn-ABERS](https://www.kaggle.com/code/carlmcbrideellis/classifier-calibration-using-venn-abers) by Carl McBride Ellis (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Websites
1. [Main website with research from Prof. Vladimir (Volodya) Vovk](http://alrw.net) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [Reliable Predictive Inference](https://sites.google.com/view/cqr) by Yaniv Romano ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Twitter
1. [Title: What Can *Conformal Inference* Offer to Statistics?](https://t.co/znZHcyyknV) by Lihua Lei, Stanford, 2022
2. [Conformalized survival analysis](https://twitter.com/lihua_lei_stat/status/1381418936739098630?s=20&t=PnfV8wnLV2bThXcKdFFbRg) by Lihua Lei, Stanford, 2021
3. [Conformal Risk Control](https://threadreaderapp.com/thread/1555616778578829312.html) by Anastasious Angelopolous, Berkeley, 2022
4. [Stable Conformal Prediction Sets](https://threadreaderapp.com/thread/1549413164835536897.html) by Eugene Ndiaye (Georgia Tech, 2022)
5. [Machine learning sucks at uncertainty quantification. But there is a solution that almost sounds too good to be true: conformal prediction](https://threadreaderapp.com/thread/1572526084981129217.html) by Cristoph Molnar (2022).
6. [How to correctly, yet efficiently model the uncertainty on predictions](https://threadreaderapp.com/thread/1584905067718647808.html) by Nico Wolf (2022)
7. [Top 10 Github libraries for Conformal Prediction](https://threadreaderapp.com/thread/1609112769017552897.html) by Valeriy Manokhin (2022)
8. [Robots That Ask For Help](https://threadreaderapp.com/thread/1677000811803443213.html) by Allen Z. Ren (2023) ๐Ÿพ๐Ÿพ๐Ÿพ๐Ÿพ๐Ÿพ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## 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. [Itโ€™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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2022](https://sites.google.com/berkeley.edu/dfuq-22/home) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
3. [Workshop on Distribution-Free Uncertainty Quantification at ICML 2021](https://icml.cc/Conferences/2021/ScheduleMultitrack?event=8373)๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [10th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2021/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [9th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2020/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
6. [8th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2019/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
7. [7th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2018/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
8. [6th Symposium on Conformal and Probabilistic Prediction with Applications](https://cml.rhul.ac.uk/copa2017/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Python

1. [Conformal Tights - A scikit-learn meta-estimator that adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor](https://github.com/radix-ai/conformal-tights) by Laurent Sorber (Radix AI) (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. ['Crรชpes' - Conformal regressors and predictive systems](https://github.com/henrikbostrom/crepes) by Henrik Bostrรถm (2021) [Paper](https://copa-conference.com/papers/COPA2022_paper_11.pdf) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [Presentation](https://copa-conference.com/presentations/COPA_2022_Presentation__crepes.pdf) by Henrik Bostroem (KTH, Sweden, 2022)
3. [Python implementation of binary and multi-class Venn-ABERS calibration](https://github.com/ip200/venn-abers) by Ivan Petej (2023) [Paper] ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [TorchCP - A library for conformal prediction](https://github.com/ml-stat-Sustech/TorchCP) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [Puncc (Predictive uncertainty calibration and conformalization)](https://github.com/deel-ai/puncc) [paper](https://proceedings.mlr.press/v204/mendil23a/mendil23a.pdf) [slides](https://copa-conference.com/presentations/COPA_2023_mouhcine_mendil_puncc.pdf) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
6. [Nixtla mlforecast](https://nixtla.github.io/mlforecast/docs/prediction_intervals.html#references) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
7. [Nixtla statsforecast](https://nixtla.github.io/statsforecast/docs/tutorials/conformalprediction.html#introduction) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
8. [Conformal Impact](https://github.com/tblume1992/ConformalImpact) by Tyler Blume (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
9.[MAPIE - Model Agnostic Prediction Interval Estimator](https://github.com/scikit-learn-contrib/MAPIE) by Quantmetry team (2021) [Paper](https://arxiv.org/pdf/2207.12274.pdf) [slides](https://arxiv.org/pdf/2207.12274.pdf) MAPIE has serious gaps in binary classifications and not recommended for binary classification problems.
10.[Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) ๐Ÿšจ The library does not seem to be actively maintained
11. [Venn-ABERS Predictor](https://github.com/ptocca/VennABERS) by Paolo Toccaceli (2019) [Paper](https://proceedings.neurips.cc/paper/2015/hash/a9a1d5317a33ae8cef33961c34144f84-Abstract.html) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
12. [Conformalized Quantile Regression](https://github.com/yromano/cqr) by Yaniv Romano (2019) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
13. [Orange3 Conformal Prediction](https://github.com/biolab/orange3-conformal)[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. [Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems](https://github.com/msesia/chr) by Mateo Sesia and Yaniv Romano (NeurIPS 2021). ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
16. [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 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
17. [Conformal time-series forecasting](https://github.com/kamilest/conformal-rnn) by Kamile ฬ‡ Stankeviciute (Cambridge, NeurIPS 2021) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
18. [EnbPI](https://github.com/hamrel-cxu/EnbPI) by Chen Xu (2021) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [Paper](https://proceedings.mlr.press/v139/xu21h.html)
19. Adaptive Conformal Predictions for Time Series](https://proceedings.mlr.press/v162/zaffran22a/zaffran22a.pdf) by Margaux Zaffran, Aymeric Dieuleveut, Olivier Fe ฬron, Yannig Goude, and Julie Josse (2022) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ [Video](https://www.youtube.com/watch?v=Yuxu9aUpVi0) [Code](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries)
20. [Conformal learning from scratch](https://github.com/marharyta-aleksandrova/conformal-learning/blob/main/theory/conformal_learning_from_scratch.ipynb) by Marharyta Aleksandrova (2021)
21. [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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
22. [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 ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
23. [PySloth - Python package for Probabilistic Prediction](https://github.com/PySloth/pysloth) by Valery Manokhin ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
24. [Conformal Prediction in KNIME](https://hub.knime.com/tuwelofstrom/spaces/Predicting%20with%20Confidence/latest/~h8R_oa6iPRTssrYs/) by Tuwe Lรถfstrรถm and Redfield AB (2022)
25. [Nonconformist](https://github.com/donlnz/nonconformist) by Henrik Linusson (2015) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
26. [SKTime](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.conformal.ConformalIntervals.html) by Franz Kiraly (2022)
27. [NeuralProphet](https://github.com/ourownstory/neural_prophet/blob/main/tutorials/feature-use/uncertainty_conformal_prediction.ipynb) (2022) ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
28. [River](https://github.com/online-ml/river/blob/0506ad73e45403638f13d66c6b2d71920d307461/river/conf/jackknife.py#L9) 2022
29. [TorchUQ](https://github.com/TorchUQ/torchuq/blob/5335c5948385c7b9bde4baefc9399d79a7cb07ef/docs/tutorials/1_c_1_conformal/1_c_1_conformal.rst#L17) (2022)
30. [https://github.com/mikekeith52/scalecast](https://github.com/mikekeith52/scalecast) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
31. [plot_utils - Plotting library for conformal prediction metrics, intended to facilitate fast testing in e.g. notebooks](https://github.com/pharmbio/plot_utils) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
32. [calibrated-explanations - Calibrated Explanations for Machine Learning Models using Venn-Abers and Conformal Predictive Systems](https://github.com/Moffran/calibrated_explanations) by Helena Lรถfstrรถm (2023)
33. [Conformers - Unofficial Conformal Language Modelling library](https://github.com/Bradley-Butcher/Conformers/) ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€
34. [Conformal Predictions from Scratch in Numpy](https://github.com/joneswack/conformal-predictions-from-scratch) by Jones Wacker (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
35. [Conformal Prediction for Digital Soil Mapping](https://github.com/nafisehkakhani/Conformal_Prediction_DSM) by Nafiseh Kakhani (2023)
36. [conformal-prediction-jan2024 - PyLadies Amsterdam](https://github.com/pyladiesams/conformal-prediction-jan2024) by Inge van den Ende (2024) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
37. [MFLES - Gradient Boosted Decomposition for time series forecasting](https://github.com/tblume1992/MFLES) by Tyler Blume (2024) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
38. [confopt - A Library for Conformal Hyperparameter Tuning](https://github.com/rick12000/confopt) by Ricardo Doyle (2024) [paper](https://arxiv.org/abs/2207.03017) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
39. [crepes-weighted Extension of crepes package, to enable weighted conformal prediction and conformal predictive systems that can handle covariate shifts](https://github.com/predict-idlab/crepes-weighted ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## R
1. [Conformal Prediction ih tidymodels](https://github.com/tidymodels/tidymodels.org/pull/23) by Max Kuhn (Posit/RStudio, 2023) [video](https://www.youtube.com/watch?v=3omi4lm1da0) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [Modeltime](https://github.com/business-science/modeltime/issues/173#issuecomment-1664681578) (2023) by Matt Dancho (Business Science, 2023) TIME SERIES ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€ ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
3. [Conformal prediction for 80+ classes of `R` models with the `marginaleffects` package](https://marginaleffects.com/articles/conformal.html) by Vincent Arel-Bundock (2023) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
4. [AdaptiveConformal](https://github.com/herbps10/AdaptiveConformal) (2023) [paper](https://hal.science/hal-04316544/) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
5. [Conformal Inference R Project](https://github.com/ryantibs/conformal) maintained by Ryan Tibshirani (2016) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
6. [Prediction Bands](https://github.com/rizbicki/predictionBands) by Rafael Izbicki and Benjamin LeRoy (2019)
7. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
8. [Online Time Series Anomaly Detectors](https://github.com/valeman/otsad) by Alaine Iturria, 2021 ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
9. [piRF - Prediction Intervals for Random Forests](https://github.com/chancejohnstone/piRF) by Chancellor Johnstone and Haozhe Zhang (2019)
10. [conformalClassification: Transductive and Inductive Conformal Predictions for Classification Problems](https://cran.r-project.org/web/packages/conformalClassification/) by Niharika Gauraha and Ola Spjuth (2019)
11. [R Package for Spatial Conformal Prediction](https://github.com/mhuiying/scp)
12. [conformalInference.multi: Conformal Inference Tools for Regression with Multivariate Response](https://cran.r-project.org/web/packages/conformalInference.multi/index.html) by Jacopo Diquigiovanni, Matteo Fontana, Aldo Solari, Simone Vantini, Paolo Vergottini, Ryan Tibshirani (2021) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
13. [Conformal: an R package to calculate prediction errors in the conformal prediction framework](https://github.com/isidroc/conformal/) by Isidro Cortes, 2019
14. [cfsurvival - An R package that implements the conformalized survival analysis methodology](https://zhimeir.github.io/cfsurvival/index.html) [Paper](https://arxiv.org/abs/2103.09763)
15. [ClusTorus: An R Package for Prediction and Clustering on the Torus by Conformal Prediction](https://journal.r-project.org/articles/RJ-2022-032/RJ-2022-032.pdf) by Seungki Hong and Sungkyu Jung (2022)
16. [conformal glm - conformal prediction for generalized linear regression models](https://github.com/DEck13/conformal.glm) by Daniel Eck (2019)
17. [caretForecast - Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms](https://github.com/Akai01/caretForecast)
18. [Localized Conformal Prediction - LCP](https://github.com/LeyingGuan/LCP)
19. [conformalbayes - Jackknife(+) Predictive Intervals for Bayesian Models](https://github.com/CoryMcCartan/conformalbayes) (2022)
20. [conformal.fd Conformal inference prediction regions for Multiple Functional response regression](https://github.com/paolo-vergo/conformal.fd) (2021)

## Julia

1. [ConformalPrediction.jl](https://github.com/pat-alt/ConformalPrediction.jl) by Patrick Altmeyer (2022) [Article - Conformal Prediction in Julia, Part I - Introduction](https://towardsdatascience.com/conformal-prediction-in-julia-351b81309e30) [Article - Conformal Prediction in Julia, Part II - How to conformalize a deep image classifier](https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) [Article - Conformal Prediction in Julia, Part III - Prediction intervals for any regression model](https://towardsdatascience.com/prediction-intervals-for-any-regression-model-306930d5ad9a)
2. [RandomForest](https://github.com/henrikbostrom/RandomForest) by Henrik Bostrรถm (2017) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ

## Other Languages
1. [LibCP -- A Library for Conformal Prediction](https://github.com/fated/libcp) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [An Implementation of Venn-ABERS predictor](https://github.com/fated/venn-abers-predictor) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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)' ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ (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) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
2. [Data Robot - Prediction Intervals via Conformal Inference](https://www.datarobot.com/ai-accelerators/prediction-intervals-via-conformal-inference/)
3. [AWS Fortuna](https://aws-fortuna.readthedocs.io/en/latest/) by Amazon, (2022) ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ
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