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We develop fast distribution-free conformal prediction algorithms for obtaining multivalid coverage on exchangeable data in the batch setting.
Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 1948
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Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 1948
Earlier work this paper cites.
De finetti-type theorems: an analytical approach
Paul Ressel · 1985
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
Earlier work this paper cites.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
Earlier work this paper cites.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Earlier work this paper cites.
Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
Earlier work this paper cites.
Learning from outcomes: Evidence-based rankings
Cynthia Dwork, Michael P Kim, Omer Reingold, Guy N Rothblum, and Gal Yona · 2019
Earlier work this paper cites.
An empirical study of rich subgroup fairness for machine learning
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
Pac confidence sets for deep neural networks via calibrated prediction
Sangdon Park, Osbert Bastani, Nikolai Matni, and Insup Lee · 2019
Cited alongside, same era.
Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
Cited alongside, same era.
Uncertainty sets for image classifiers using conformal prediction
Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik · 2020
Cited alongside, same era.
The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani · 2020
Bayes-optimal prediction with frequentist coverage control
Peter Hoff · 2021
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Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2021
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Multi-group agnostic pac learnability
Guy N Rothblum and Gal Yona · 2021
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Practical adversarial multivalid conformal prediction
Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, and Aaron Roth · 2022
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Training-conditional coverage for distribution-free predictive inference
Michael Bian and Rina Foygel Barber · 2022
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Cited alongside, same era.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
Cited alongside, same era.
Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
Cited alongside, same era.
Low-degree multicalibration
Parikshit Gopalan, Michael P Kim, Mihir A Singhal, and Shengjia Zhao
Cited in the paper.
With malice toward none: Assessing uncertainty via equalized coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Candès
Cited in the paper.
Classification with valid and adaptive coverage
Yaniv Romano, Matteo Sesia, and Emmanuel Candes
Cited in the paper.
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, and Aaron Roth · 2022
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Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
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Online Multivalid Learning: Means, Moments, and Prediction Intervals
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, and Aaron Roth · 2022
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