Optimal Thresholding Linear Bandit
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 11 Feb 2024.
Builds on
8 of 19 references.
References · 8
- 2016Optimal Best Arm Identification with Fixed Confidencecited by 233
- 2014Best-Arm Identification in Linear Banditscited by 98
- 2019Sequential Experimental Design for Transductive Linear Banditscited by 88
- 2020Optimal Best-arm Identification in Linear Banditscited by 70
- 2020Gamification of Pure Exploration for Linear Banditscited by 61
- 2019Pure Exploration with Multiple Correct Answerscited by 60
- 2017Fully adaptive algorithm for pure exploration in linear banditscited by 43
- 2017Good Arm Identification via Bandit Feedbackcited by 29
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