A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2332-2340, 2021.

Abstract

We study multiple-source domain adaptation, when the learner has access to abundant labeled data from multiple-source domains and limited labeled data from the target domain. We analyze existing algorithms for this problem, and propose a novel algorithm based on model selection. Our algorithms are efficient, and experiments on real data-sets empirically demonstrate their benefits.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-mansour21a, title = { A Theory of Multiple-Source Adaptation with Limited Target Labeled Data }, author = {Mansour, Yishay and Mohri, Mehryar and Ro, Jae and Theertha Suresh, Ananda and Wu, Ke}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2332--2340}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/mansour21a/mansour21a.pdf}, url = {https://proceedings.mlr.press/v130/mansour21a.html}, abstract = { We study multiple-source domain adaptation, when the learner has access to abundant labeled data from multiple-source domains and limited labeled data from the target domain. We analyze existing algorithms for this problem, and propose a novel algorithm based on model selection. Our algorithms are efficient, and experiments on real data-sets empirically demonstrate their benefits. } }
Endnote
%0 Conference Paper %T A Theory of Multiple-Source Adaptation with Limited Target Labeled Data %A Yishay Mansour %A Mehryar Mohri %A Jae Ro %A Ananda Theertha Suresh %A Ke Wu %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-mansour21a %I PMLR %P 2332--2340 %U https://proceedings.mlr.press/v130/mansour21a.html %V 130 %X We study multiple-source domain adaptation, when the learner has access to abundant labeled data from multiple-source domains and limited labeled data from the target domain. We analyze existing algorithms for this problem, and propose a novel algorithm based on model selection. Our algorithms are efficient, and experiments on real data-sets empirically demonstrate their benefits.
APA
Mansour, Y., Mohri, M., Ro, J., Theertha Suresh, A. & Wu, K.. (2021). A Theory of Multiple-Source Adaptation with Limited Target Labeled Data . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2332-2340 Available from https://proceedings.mlr.press/v130/mansour21a.html.

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