Regret Bounds for Lifelong Learning

Actes d’une conférence

Regret Bounds for Lifelong Learning

Année

2017

Auteurs

ALQUIER Pierre, MAI The Tien, PONTIL Massimiliano

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.

ALQUIER, P., MAI, T.T. et PONTIL, M. (2017). Regret Bounds for Lifelong Learning. Dans: 20th International Conference on Artificial Intelligence and Statistics (AIStat’17). Proceedings of Machine Learning Research.