Alquier, P;
Mai, TT;
Pontil, M;
(2017)
Regret Bounds for Lifelong Learning.
In: Singh, A and Zhu, XJ, (eds.)
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017).
(pp. pp. 261-269).
PMLR (Proceedings of Machine Learning Research): Fort Lauderdale, FL, USA.
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Abstract
We consider the problem of transfer learning in an online setting. Di↵erent 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 withintask 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/pm) bounds to O(1/m), where m is the per task sample size.
| Type: | Proceedings paper |
|---|---|
| Title: | Regret Bounds for Lifelong Learning |
| Event: | 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 20-22 April 2017, Fort Lauderdale, FL, USA |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://jmlr.org/proceedings/papers/v54/ |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10073437 |
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