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Active Learning under Label Shift

Zhao, Eric and Liu, Anqi and Anandkumar, Animashree and Yue, Yisong (2021) Active Learning under Label Shift. Proceedings of Machine Learning Research, 130 . pp. 3412-3420. ISSN 1938-7228.

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We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling and propose their combined usage in active learning. Our method is known as Mediated Active Learning under Label Shift (MALLS). It balances the bias from class-balanced sampling and the variance from importance weighting. We prove sample complexity and generalization guarantees for MALLS which show active learning reduces asymptotic sample complexity even under arbitrary label shift. We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Zhao, Eric0000-0002-9595-0150
Yue, Yisong0000-0001-9127-1989
Additional Information:© 2021 by the author(s). Anqi Liu is supported by the PIMCO Postdoctoral Fellowship. Prof. Anandkumar is supported by Bren endowed Chair, faculty awards from Microsoft, Google, and Adobe, Beyond Limits, and LwLL grants. This work is also supported by funding from Raytheon and NASA TRISH.
Funding AgencyGrant Number
PIMCO Postdoctoral FellowshipUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Google Faculty Research AwardUNSPECIFIED
Learning with Less Labels (LwLL)UNSPECIFIED
Raytheon CompanyUNSPECIFIED
Record Number:CaltechAUTHORS:20201110-074357009
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106577
Deposited By: Tony Diaz
Deposited On:10 Nov 2020 16:22
Last Modified:30 Aug 2021 20:59

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