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Poster
Meta-learning with Stochastic Linear Bandits
Leonardo Cella · Alessandro Lazaric · Massimiliano Pontil

Tue Jul 14 01:00 PM -- 01:45 PM & Wed Jul 15 12:00 AM -- 12:45 AM (PDT) @
in Poster Session 7 »

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.

Keywords: Transfer and Multitask Learning · Online Learning / Bandits · Transfer, Multitask and Meta-learning

Author Information

Leonardo Cella (University of Milan)
Alessandro Lazaric (Facebook AI Research)
Massimiliano Pontil (Istituto Italiano di Tecnologia and University College London)

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