May 10, 2024, 4:42 a.m. | Yu Liu, Yunlu Shu, Tianyu Wang

cs.LG updates on

arXiv:2405.05733v1 Announce Type: cross
Abstract: This paper studies batched bandit learning problems for nondegenerate functions. We introduce an algorithm that solves the batched bandit problem for nondegenerate functions near-optimally. More specifically, we introduce an algorithm, called Geometric Narrowing (GN), whose regret bound is of order $\widetilde{{\mathcal{O}}} ( A_{+}^d \sqrt{T} )$. In addition, GN only needs $\mathcal{O} (\log \log T)$ batches to achieve this regret. We also provide lower bound analysis for this problem. More specifically, we prove that over some …

abstract algorithm arxiv cs.lg functions near paper stochastic studies type

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