2023-04-11-bhatt23b.md

title	abstract	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Piecewise Stationary Bandits under Risk Criteria	Piecewise stationary stochastic multi-armed bandits have been extensively explored in the risk-neutral and sub-Gaussian setting. In this work, we consider a multi-armed bandit framework in which the reward distributions are heavy-tailed and non-stationary, and evaluate the performance of algorithms using general risk criteria. Specifically, we make the following contributions: (i) We first propose a non-parametric change detection algorithm that can detect general distributional changes in heavy-tailed distributions. (ii)We then propose a truncation-based UCB-type bandit algorithm integrating the above regime change detection algorithm to minimize the regret of the non-stationary learning problem. (iii) Finally, we establish the regret bounds for the proposed bandit algorithm by characterizing the statistical properties of the general change detection algorithm, along with a novel regret analysis.	Regular Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bhatt23b	0	Piecewise Stationary Bandits under Risk Criteria	4313	4335	4313-4335	4313	false	Bhatt, Sujay and Fang, Guanhua and Li, Ping	given family Sujay Bhatt given family Guanhua Fang given family Ping Li	2023-04-11		Proceedings of The 26th International Conference on Artificial Intelligence and Statistics	206	inproceedings	date-parts 2023 4 11	https://proceedings.mlr.press/v206/bhatt23b/bhatt23b.pdf