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On the Optimal Regret of Locally Private Linear Contextual Bandit
April 16, 2024, 4:43 a.m. | Jiachun Li, David Simchi-Levi, Yining Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing interest in designing \emph{locally private} linear contextual bandit algorithms, where sensitive information contained in contexts and rewards is protected against leakage to the general public. While the classical linear contextual bandit algorithm admits cumulative regret upper bounds of $\tilde O(\sqrt{T})$ via multiple alternative methods, it has remained open whether …
abstract algorithms arxiv cs.cr cs.lg designing functions information linear online learning research stat.ml type
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