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Thresholding Bandit with Optimal Aggregate Regret by Tao, Chao; Blanco, Saùl; Peng, Jian; Zhou, Yuan is a scholarly article available to read on EtoBox.

What is Thresholding Bandit with Optimal Aggregate Regret about?

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.

Author

Tao, Chao; Blanco, Saùl; Peng, Jian; Zhou, Yuan

Published

2019

Language

EN