Lifelong Optimization with Low Regret

Yi-Shan Wu, Po-An Wang, Chi-Jen Lu
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:448-456, 2019.

Abstract

In this work, we study a problem arising from two lines of works: online optimization and lifelong learning. In the problem, there is a sequence of tasks arriving sequentially, and within each task, we have to make decisions one after one and then suffer corresponding losses. The tasks are related as they share some common representation, but they are different as each requires a different predictor on top of the representation. As learning a representation is usually costly in lifelong learning scenarios, the goal is to learn it continuously through time across different tasks, making the learning of later tasks easier than previous ones. We provide such learning algorithms with good regret bounds which can be seen as natural generalization of prior works on online optimization.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-wu19a, title = {Lifelong Optimization with Low Regret}, author = {Wu, Yi-Shan and Wang, Po-An and Lu, Chi-Jen}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {448--456}, 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/wu19a/wu19a.pdf}, url = {https://proceedings.mlr.press/v89/wu19a.html}, abstract = {In this work, we study a problem arising from two lines of works: online optimization and lifelong learning. In the problem, there is a sequence of tasks arriving sequentially, and within each task, we have to make decisions one after one and then suffer corresponding losses. The tasks are related as they share some common representation, but they are different as each requires a different predictor on top of the representation. As learning a representation is usually costly in lifelong learning scenarios, the goal is to learn it continuously through time across different tasks, making the learning of later tasks easier than previous ones. We provide such learning algorithms with good regret bounds which can be seen as natural generalization of prior works on online optimization.} }
Endnote
%0 Conference Paper %T Lifelong Optimization with Low Regret %A Yi-Shan Wu %A Po-An Wang %A Chi-Jen Lu %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-wu19a %I PMLR %P 448--456 %U https://proceedings.mlr.press/v89/wu19a.html %V 89 %X In this work, we study a problem arising from two lines of works: online optimization and lifelong learning. In the problem, there is a sequence of tasks arriving sequentially, and within each task, we have to make decisions one after one and then suffer corresponding losses. The tasks are related as they share some common representation, but they are different as each requires a different predictor on top of the representation. As learning a representation is usually costly in lifelong learning scenarios, the goal is to learn it continuously through time across different tasks, making the learning of later tasks easier than previous ones. We provide such learning algorithms with good regret bounds which can be seen as natural generalization of prior works on online optimization.
APA
Wu, Y., Wang, P. & Lu, C.. (2019). Lifelong Optimization with Low Regret. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:448-456 Available from https://proceedings.mlr.press/v89/wu19a.html.

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