Abstract Information-directed sampling (IDS) has revealed its potential as a data-efficient
algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for …

In nonstationary bandit learning problems, the decision-maker must continually gather
information and adapt their action selection as the latent state of the environment evolves. In …

In this paper, we prove state-of-the-art Bayesian regret bounds for Thompson Sampling in
reinforcement learning in a multitude of settings. We present a refined analysis of the …

Abstract Information-directed sampling (IDS) has recently demonstrated its potential as a
data-efficient reinforcement learning algorithm. However, it is still unclear what is the right …

In this paper, we address the stochastic contextual linear bandit problem, where a decision
maker is provided a context (a random set of actions drawn from a distribution). The …

We investigate the extent to which offline demonstration data can improve online learning. It
is natural to expect some improvement, but the question is how, and by how much? We …

We consider the stochastic linear contextual bandit problem with high-dimensional features.
We analyze the Thompson sampling algorithm using special classes of sparsity-inducing …

We explore a stochastic contextual linear bandit problem where the agent observes a noisy,
corrupted version of the true context through a noise channel with an unknown noise …

The growing interest in complex decision-making and language modeling problems
highlights the importance of sample-efficient learning over very long horizons. This work …

It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity
which is much smaller than the ambient dimension, the worst-case minimax regret is …