Multi-Player Bandits: The Adversarial Case

Multi-Player Bandits: The Adversarial Case

21 February 2019

Papers citing "Multi-Player Bandits: The Adversarial Case"

12 / 12 papers shown

Title
Learning to Control Unknown Strongly Monotone Games Siddharth Chandak Ilai Bistritz Nicholas Bambos 36 3 0 30 Jun 2024
Decision Market Based Learning For Multi-agent Contextual Bandit Problems Wenlong Wang T. Pfeiffer 29 1 0 01 Dec 2022
A survey on multi-player bandits Etienne Boursier Vianney Perchet 32 13 0 29 Nov 2022
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit Aldo Pacchiano Peter L. Bartlett Michael I. Jordan 24 5 0 08 Nov 2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization Chengshuai Shi Wei Xiong Cong Shen Jing Yang 25 23 0 27 Oct 2021
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions Junyan Liu Shuai Li Dapeng Li 23 6 0 08 Jun 2021
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications Chengshuai Shi Cong Shen AAML 19 9 0 02 Nov 2020
Coordination without communication: optimal regret in two players multi-armed bandits Sébastien Bubeck Thomas Budzinski 12 23 0 14 Feb 2020
Adaptive Sampling for Stochastic Risk-Averse Learning Sebastian Curi Kfir Y. Levy Stefanie Jegelka Andreas Krause 24 52 0 28 Oct 2019
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits Yogev Bar-On Yishay Mansour 13 41 0 07 Jul 2019
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without Sébastien Bubeck Yuanzhi Li Yuval Peres Mark Sellke 8 45 0 28 Apr 2019
Determinantal point processes for machine learning Alex Kulesza B. Taskar 176 1,125 0 25 Jul 2012