Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …

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

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

We consider the adversarial linear contextual bandit problem, where the loss vectors are
selected fully adversarially and the per-round action set (ie the context) is drawn from a fixed …

Thompson sampling has been widely used for contextual bandit problems due to the
flexibility of its modeling power. However, a general theory for this class of methods in the …

We study the linear contextual bandit problem in the presence of adversarial corruption,
where the reward at each round is corrupted by an adversary, and the corruption level (ie …

Despite the significant interest and progress in reinforcement learning (RL) problems with
adversarial corruption, current works are either confined to the linear setting or lead to an …

A central problem in sequential decision making is to develop algorithms that are practical
and computationally efficient, yet support the use of flexible, general-purpose models …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

We develop a model selection approach to tackle reinforcement learning with adversarial
corruption in both transition and reward. For finite-horizon tabular MDPs, without prior …