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Meta-learning with Stochastic Linear Bandits (2005.08531v1)

Published 18 May 2020 in stat.ML and cs.LG

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.

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Authors (3)
  1. Leonardo Cella (14 papers)
  2. Alessandro Lazaric (78 papers)
  3. Massimiliano Pontil (97 papers)
Citations (53)

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