This study introduces the α-Linear-Contextual (LC)-Tsallis-INF algorithm for linear contextual bandits. It addresses the linear contextual bandit problem with adversarial corruption, proposing a Best-of-Both-Worlds algorithm. The content discusses the existing studies, assumptions, and the proposed algorithm's structure and parameters. It provides detailed insights into the linear contextual bandit problem, the proposed algorithm, and the theoretical analysis of regret bounds.
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arxiv.org
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by Masahiro Kat... : arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.03219.pdfDaha Derin Sorular