2021-03-18-chen21d.md

title	abstract	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
Active Online Learning with Hidden Shifting Domains	Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains. For online linear regression with oblivious adversaries, we provide a tight tradeoff that depends on the durations and dimensionalities of the hidden domains. Our algorithm can adaptively deal with interleaving spans of inputs from different domains. We also generalize our results to non-linear regression for hypothesis classes with bounded eluder dimension and adaptive adversaries. Experiments on synthetic and realistic datasets demonstrate that our algorithm achieves lower regret than uniform queries and greedy queries with equal labeling budget.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen21d	0	Active Online Learning with Hidden Shifting Domains	2053	2061	2053-2061	2053	false	Chen, Yining and Luo, Haipeng and Ma, Tengyu and Zhang, Chicheng	given family Yining Chen given family Haipeng Luo given family Tengyu Ma given family Chicheng Zhang	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/chen21d/chen21d.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/chen21d/chen21d-supp.pdf