Regret Bounds for Lifelong Learning

Pierre Alquier, The Tien Mai, Massimiliano Pontil
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:261-269, 2017.

Abstract

We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learning strategy which refines the underlying data representation used by the within-task algorithm, thereby transferring information from one task to the next. We show that when the within-task algorithm comes with some regret bound, our strategy inherits this good property. Our bounds are in expectation for a general loss function, and uniform for a convex loss. We discuss applications to dictionary learning and finite set of predictors. In the latter case, we improve previous $O(1/\sqrtm)$ bounds to $O(1/m)$, where $m$ is the per task sample size.

Cite this Paper


BibTeX
@InProceedings{pmlr-v54-alquier17a, title = {{Regret Bounds for Lifelong Learning}}, author = {Alquier, Pierre and Mai, The Tien and Pontil, Massimiliano}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {261--269}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/alquier17a/alquier17a.pdf}, url = {https://proceedings.mlr.press/v54/alquier17a.html}, abstract = {We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learning strategy which refines the underlying data representation used by the within-task algorithm, thereby transferring information from one task to the next. We show that when the within-task algorithm comes with some regret bound, our strategy inherits this good property. Our bounds are in expectation for a general loss function, and uniform for a convex loss. We discuss applications to dictionary learning and finite set of predictors. In the latter case, we improve previous $O(1/\sqrtm)$ bounds to $O(1/m)$, where $m$ is the per task sample size.} }
Endnote
%0 Conference Paper %T Regret Bounds for Lifelong Learning %A Pierre Alquier %A The Tien Mai %A Massimiliano Pontil %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-alquier17a %I PMLR %P 261--269 %U https://proceedings.mlr.press/v54/alquier17a.html %V 54 %X We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learning strategy which refines the underlying data representation used by the within-task algorithm, thereby transferring information from one task to the next. We show that when the within-task algorithm comes with some regret bound, our strategy inherits this good property. Our bounds are in expectation for a general loss function, and uniform for a convex loss. We discuss applications to dictionary learning and finite set of predictors. In the latter case, we improve previous $O(1/\sqrtm)$ bounds to $O(1/m)$, where $m$ is the per task sample size.
APA
Alquier, P., Mai, T.T. & Pontil, M.. (2017). Regret Bounds for Lifelong Learning. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:261-269 Available from https://proceedings.mlr.press/v54/alquier17a.html.

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