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We study a novel pure exploration problem: the $ε$-Thresholding Bandit Problem (TBP) with fixed confidence in stochastic linear bandits. We prove a lower bound for the sample complexity and extend an algorithm designed for Best Arm Identification in the linear case to TBP that is asymptotically optimal.
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@article{rivera2024optimal,
title = {Optimal Thresholding Linear Bandit},
author = {Eduardo Ochoa Rivera and Ambuj Tewari},
year = {2024},
eprint = {2402.09467},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2402.09467v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2402.09467)