[Theory Lunch 04/02] Mengxiao Zhang: Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

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Haoming Li

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Mar 29, 2021, 2:56:38 AM3/29/21
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USC CS Theory Lunch:

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Speaker: Mengxiao Zhang (zha...@usc.edu)
Time: 04/02/21 11:45am PST
Location: https://usc.zoom.us/j/94386654763

Abstract:

In this work, we consider the linear bandit problem with a finite and fixed action set. We develop linear bandit algorithms with provable regret guarantees that automatically adapt to different environments, including (possibly corrupted) stochastic environment and adversarial environment. (https://arxiv.org/pdf/2102.05858.pdf)
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