2022-06-28-abernethy22a.md

title	booktitle	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 Sampling for Min-Max Fairness	Proceedings of the 39th International Conference on Machine Learning	We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	abernethy22a	0	Active Sampling for Min-Max Fairness	53	65	53-65	53	false	Abernethy, Jacob D and Awasthi, Pranjal and Kleindessner, Matth{\"a}us and Morgenstern, Jamie and Russell, Chris and Zhang, Jie	given family Jacob D Abernethy given family Pranjal Awasthi given family Matthäus Kleindessner given family Jamie Morgenstern given family Chris Russell given family Jie Zhang	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/abernethy22a/abernethy22a.pdf