2022-06-28-bao22a.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
Learning Stable Classifiers by Transferring Unstable Features	Proceedings of the 39th International Conference on Machine Learning	While unbiased machine learning models are essential for many applications, bias is a human-defined concept that can vary across tasks. Given only input-label pairs, algorithms may lack sufficient information to distinguish stable (causal) features from unstable (spurious) features. However, related tasks often share similar biases – an observation we may leverage to develop stable classifiers in the transfer setting. In this work, we explicitly inform the target classifier about unstable features in the source tasks. Specifically, we derive a representation that encodes the unstable features by contrasting different data environments in the source task. We achieve robustness by clustering data of the target task according to this representation and minimizing the worst-case risk across these clusters. We evaluate our method on both text and image classifications. Empirical results demonstrate that our algorithm is able to maintain robustness on the target task for both synthetically generated environments and real-world environments. Our code is available at https://github.com/YujiaBao/Tofu.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bao22a	0	Learning Stable Classifiers by Transferring Unstable Features	1483	1507	1483-1507	1483	false	Bao, Yujia and Chang, Shiyu and Barzilay, Dr.Regina	given family Yujia Bao given family Shiyu Chang given family Dr.Regina Barzilay	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bao22a/bao22a.pdf