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arXiv · 1905.11046

Thresholding Bandit with Optimal Aggregate Regret

Chao Tao·Saùl Blanco·Jian Peng·Yuan Zhou
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Abstract

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algorithm is instance-wise asymptotically optimal. We also provide comprehensive empirical results to demonstrate the algorithm's superior performance over existing algorithms under a variety of different scenarios.

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BibTeXRIS

Chao Tao, Saùl Blanco, Jian Peng, Yuan Zhou. 2019-05-27. Thresholding Bandit with Optimal Aggregate Regret. https://arxiv.org/abs/1905.11046

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