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We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities
V. N. Vapnik and A.Y. Chervonenkis · 1971
Earlier work this paper cites.
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Ellen L. Hahne · 1991
Earlier work this paper cites.
An Introduction to Computational Learning Theory
Michael J. Kearns and Umesh V. Vazirani · 1994
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Yoav Freund and Robert E. Schapire · 1996
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Earlier work this paper cites.
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A unified framework for max-min and min-max fairness with applications
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Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2008
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Max-min fairness and its applications to routing and load-balancing in communication networks: a tutorial
D. Nace and M. Pióro · 2008
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An approximation algorithm for max-min fair allocation of indivisible goods
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Wlodzimierz Ogryczak, Hanan Luss, Dritan Nace, and Michał Pióro · 2014
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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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Michael P Kim, Amirata Ghorbani, and James Zou · 2019
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Average individual fairness: Algorithms, generalization and experiments
Saeed Sharifi-Malvajerdi, Michael Kearns, and Aaron Roth · 2019
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Fairness without harm: Decoupled classifiers with preference guarantees
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An algorithmic framework for fairness elicitation
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Fairness without demographics through adversarially reweighted learning, 2020
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi · 2020
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Minimax Pareto fairness: A multi objective perspective
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Algorithmic fairness: Choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2020
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Online multivalid learning: Means, moments, and prediction intervals
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M Pai, and Aaron Roth · 2021
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