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

We study online learning with bandit feedback across multiple tasks, with the goal of
improving average performance across tasks if they are similar according to some natural …

We address the problem of lifelong fixed-budget best-arm identification (BAI), which arises in
realistic sequential A/B testing scenarios where the value of each arm is correlated across …

We study meta-learning for adversarial multi-armed bandits. We consider the online-within-
online setup, in which a player (learner) encounters a sequence of multi-armed bandit …

A continuity structure of correlations among arms in multi-armed bandit can bring a
significant acceleration of exploration and reduction of regret, in particular, when there are …

We study lifelong learning in linear bandits, where a learner interacts with a sequence of
linear bandit tasks whose parameters lie in an $ m $-dimensional subspace of $\mathbb …