Meta-Learning aims to speed up the learning process on new tasks by acquiring useful
inductive biases from datasets of related learning tasks. While, in practice, the number of …

The contextual linear bandit is an important online learning problem where given arm
features, a learning agent selects an arm at each round to maximize the cumulative rewards …

As representation learning becomes a powerful technique to reduce sample complexity in
reinforcement learning (RL) in practice, theoretical understanding of its advantage is still …

We study the problem of transfer-learning in the setting of stochastic linear contextual bandit
tasks. We consider that a low dimensional linear representation is shared across the tasks …

Abstract Model selection in the context of bandit optimization is a challenging problem, as it
requires balancing exploration and exploitation not only for action selection, but also for …

Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central
challenge in sequential decision-making tasks, such as bandits and model-based …

Abstract Machine learning algorithms are often repeatedly. applied to problems with similar
structure over and over again. We focus on solving a sequence of bandit optimization tasks …

A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on
what prior knowledge of the problem is available and what actions can be taken to learn …

We study the problem of meta-learning several contextual stochastic bandits tasks by
leveraging their concentration around a low dimensional affine subspace, which we learn …

Meta-learning seeks to build algorithms that rapidly learn how to solve new learning
problems based on previous experience. In this paper we investigate meta-learning in the …