Related papers: Meta-learning with Stochastic Linear Bandits

Meta-learning with Stochastic Linear Bandits

URL: http://arxiv.org/abs/2005.08531v1
Date: Mon, 18 May 2020 08:41:39 GMT
Title: Meta-learning with Stochastic Linear Bandits
Authors: Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Abstract summary: 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 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.
Score: 120.43000970418939
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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