arXiv:2502. 08870v2 Announce Type: replace Abstract: We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation.
By Marc Abeille, David Janz, Ciara Pike-Burke
arXiv:2603. 09276v2 Announce Type: replace-cross Abstract: We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP.
By Shion Takeno, Shogo Iwazaki
arXiv:2608. 16492v1 Announce Type: cross Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization.
By Shion Takeno, Shogo Iwazaki
arXiv:2608. 15848v1 Announce Type: cross Abstract: We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory matrix.
By Heesang Ann, Hyunjun Choi, Taehyun Hwang, Younghoon Shin, Haeju Cheong, Min-hwan Oh
arXiv:2606. 08028v1 Announce Type: new Abstract: We study high-probability regret bounds for online convex optimization (OCO) with strongly convex losses and establish three results that resolve open questions at the intersection of noise adaptivity, feedback structure, and constraint satisfaction.
By Wentao Zhang, Yutong Zhang, Wentao Mo
arXiv:2607. 23679v1 Announce Type: new Abstract: Recent years have witnessed increasing interests in tackling heteroscedastic noise in bandits and reinforcement learning.
By Heyang Zhao, Tianyuan Jin, Weixin Wang, Vincent Y. F. Tan, Pan Xu, Quanquan Gu