Data-Driven Approach to Multiple-Source Domain Adaptation

Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:3487-3496, 2019.

Abstract

A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-stojanov19b, title = {Data-Driven Approach to Multiple-Source Domain Adaptation}, author = {Stojanov, Petar and Gong, Mingming and Carbonell, Jaime and Zhang, Kun}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {3487--3496}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/stojanov19b/stojanov19b.pdf}, url = {https://proceedings.mlr.press/v89/stojanov19b.html}, abstract = {A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.} }
Endnote
%0 Conference Paper %T Data-Driven Approach to Multiple-Source Domain Adaptation %A Petar Stojanov %A Mingming Gong %A Jaime Carbonell %A Kun Zhang %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-stojanov19b %I PMLR %P 3487--3496 %U https://proceedings.mlr.press/v89/stojanov19b.html %V 89 %X A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.
APA
Stojanov, P., Gong, M., Carbonell, J. & Zhang, K.. (2019). Data-Driven Approach to Multiple-Source Domain Adaptation. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:3487-3496 Available from https://proceedings.mlr.press/v89/stojanov19b.html.

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