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Meta-learning with Stochastic Linear Bandits

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. Green open access

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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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