2019-06-25-auer19b.md

abstract	section	title	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
This joint extended abstract introduces and compares the results of (Auer et al., 2019) and (Chen et al., 2019), both of which resolve the problem of achieving optimal dynamic regret for non-stationary bandits without prior information on the non-stationarity. Specifically, Auer et al. (2019) resolve the problem for the traditional multi-armed bandits setting, while Chen et al. (2019) give a solution for the more general contextual bandits setting. Both works extend the key idea of (Auer et al., 2018) developed for a simpler two-armed setting.	contributed	Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information	inproceedings	Proceedings of Machine Learning Research	auer19b	0	Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information	159	163	159-163	159	false	Auer, Peter and Chen, Yifang and Gajane, Pratik and Lee, Chung-Wei and Luo, Haipeng and Ortner, Ronald and Wei, Chen-Yu	given family Peter Auer given family Yifang Chen given family Pratik Gajane given family Chung-Wei Lee given family Haipeng Luo given family Ronald Ortner given family Chen-Yu Wei	2019-06-25		PMLR	Proceedings of the Thirty-Second Conference on Learning Theory	99	inproceedings	date-parts 2019 6 25	http://proceedings.mlr.press/v99/auer19b/auer19b.pdf