Cella, L;
Lazaric, A;
Pontil, M;
(2020)
Meta-learning with Stochastic Linear Bandits.
In: Daumé III, H and Singh, A, (eds.)
Proceedings of the 37th International Conference on Machine Learning.
(pp. pp. 1337-1347).
Proceedings of Machine Learning Research (PMLR): Virtual conference.
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Abstract
We investigate meta-learning procedures in the setting of stochastic linear bandits tasks. The goal is to select a learning algorithm which works well on average over a class of bandits tasks, that are sampled from a task-distribution. Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm, where the regularization is a square euclidean distance to a bias vector. We first study the benefit of the biased OFUL algorithm in terms of regret minimization. We then propose two strategies to estimate the bias within the learning-to-learn setting. We show both theoretically and experimentally, that when the number of tasks grows and the variance of the task-distribution is small, our strategies have a significant advantage over learning the tasks in isolation.
| Type: | Proceedings paper |
|---|---|
| Title: | Meta-learning with Stochastic Linear Bandits |
| Event: | 37th International Conference on Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v119/cella20a.html |
| 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/10128626 |
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