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We give a simple, generic conformal prediction method for sequential prediction that achieves target empirical coverage guarantees against adversarially chosen data.
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev · 1986
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On-line confidence machines are well-calibrated
Vladimir Vovk · 2002
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Exact and robust conformal inference methods for predictive machine learning with dependent data
Victor Chernozhukov, Kaspar Wüthrich, and Zhu Yinchu · 2018
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Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
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An empirical study of rich subgroup fairness for machine learning
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
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Uncertainty sets for image classifiers using conformal prediction
Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik · 2020
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The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani · 2020
Forecast hedging and calibration
Dean P Foster and Sergiu Hart · 2021
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Adaptive conformal inference under distribution shift
Isaac Gibbs and Emmanuel Candes · 2021
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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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Conformalized online learning: Online calibration without a holdout set
Shai Feldman, Stephen Bates, and Yaniv Romano · 2022
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Online Multivalid Learning: Means, Moments, and Prediction Intervals
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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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
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Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, and Aaron Roth · 2022
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Adaptive conformal predictions for time series
Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron, Yannig Goude, and Julie Josse · 2022
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