AISTATS 2021poster31 citations
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu
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.
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. }
}