Regret Bounds for Lifelong Learning

Pierre Alquier; The Tien Mai; Massimiliano Pontil

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arXiv · 1610.08628

Regret Bounds for Lifelong Learning

Pierre Alquier·The Tien Mai·Massimiliano Pontil

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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 $O(1/\sqrt{m})$ bounds to $O(1/m)$ where $m$ is the per task sample size.

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Pierre Alquier, The Tien Mai, Massimiliano Pontil. 2016-10-27. Regret Bounds for Lifelong Learning. https://arxiv.org/abs/1610.08628

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