A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

This paper provides a theoretical understanding of deep Q-Network (DQN) with the
$\varepsilon $-greedy exploration in deep reinforcement learning. Despite the tremendous …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

We study the problem of online learning in a two-player decentralized cooperative
Stackelberg game. In each round, the leader first takes an action, followed by the follower …

In online reinforcement learning (RL), efficient exploration remains particularly challenging
in high-dimensional environments with sparse rewards. In low-dimensional environments …

This paper provides a theoretical study of deep neural function approximation in
reinforcement learning (RL) with the $\epsilon $-greedy exploration under the online setting …

Real-world machine learning applications often involve deploying neural networks to
domains that are not seen in the training time. Hence, we need to understand the …

We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual
bandits with binary losses and adversarially-selected contexts. We adapt the information …

We study fast rates of convergence in the setting of nonparametric online regression, namely
where regret is defined with respect to an arbitrary function class which has bounded …

Recent papers on the theory of representation learning has shown the importance of a
quantity called diversity when generalizing from a set of source tasks to a target task. Most of …