Optimal Thresholding Linear Bandit
Machine Learning
2024-02-16 v1 Machine Learning
Abstract
We study a novel pure exploration problem: the -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.
Cite
@article{arxiv.2402.09467,
title = {Optimal Thresholding Linear Bandit},
author = {Eduardo Ochoa Rivera and Ambuj Tewari},
journal= {arXiv preprint arXiv:2402.09467},
year = {2024}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2006.16073 by other authors