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Hauptverfasser: Dwork, Cynthia, Lee, Daniel, Lin, Huijia, Tankala, Pranay
Format: Preprint
Veröffentlicht: 2023
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Online-Zugang:https://arxiv.org/abs/2301.08837
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author Dwork, Cynthia
Lee, Daniel
Lin, Huijia
Tankala, Pranay
author_facet Dwork, Cynthia
Lee, Daniel
Lin, Huijia
Tankala, Pranay
contents We identify and explore connections between the recent literature on multi-group fairness for prediction algorithms and the pseudorandomness notions of leakage-resilience and graph regularity. We frame our investigation using new variants of multicalibration based on statistical distance and closely related to the concept of outcome indistinguishability. Adopting this perspective leads us not only to new, more efficient algorithms for multicalibration, but also to our graph theoretic results and a proof of a novel hardcore lemma for real-valued functions.
format Preprint
id arxiv_https___arxiv_org_abs_2301_08837
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle From Pseudorandomness to Multi-Group Fairness and Back
Dwork, Cynthia
Lee, Daniel
Lin, Huijia
Tankala, Pranay
Machine Learning
Computational Complexity
We identify and explore connections between the recent literature on multi-group fairness for prediction algorithms and the pseudorandomness notions of leakage-resilience and graph regularity. We frame our investigation using new variants of multicalibration based on statistical distance and closely related to the concept of outcome indistinguishability. Adopting this perspective leads us not only to new, more efficient algorithms for multicalibration, but also to our graph theoretic results and a proof of a novel hardcore lemma for real-valued functions.
title From Pseudorandomness to Multi-Group Fairness and Back
topic Machine Learning
Computational Complexity
url https://arxiv.org/abs/2301.08837