My fair bandit: Distributed learning of max-min fairness with multi-player bandits

Ilai Bistritz*, Tavor Z. Baharav*, Amir Leshem, Nicholas Bambos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Consider N cooperative but non-communicating players where each plays one out of M arms for T turns. Players have different utilities for each arm, representable as an N × M matrix. These utilities are unknown to the players. In each turn players select an arm and receive a noisy observation of their utility for it. However, if any other players selected the same arm that turn, all colliding players will receive zero utility due to the conflict. No other communication or coordination between the players is possible. Our goal is to design a distributed algorithm that learns the matching between players and arms that achieves max-min fairness while minimizing the regret. We present an algorithm and prove that it is regret optimal up to a log log T factor. This is the first max-min fairness multi-player bandit algorithm with (near) order optimal regret.

Original languageAmerican English
Title of host publication37th International Conference on Machine Learning, ICML 2020
EditorsHal Daume, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Pages907-917
Number of pages11
ISBN (Electronic)9781713821120
StatePublished - 2020
Externally publishedYes
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

Name37th International Conference on Machine Learning, ICML 2020
VolumePartF168147-2

Conference

Conference37th International Conference on Machine Learning, ICML 2020
CityVirtual, Online
Period13/07/2018/07/20

Bibliographical note

Publisher Copyright:
© 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

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