This paper presents a novel federated linear contextual bandits model, where individual
clients face different $ K $-armed stochastic bandits coupled through common global …

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 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 …

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 …

In this work, we develop linear bandit algorithms that automatically adapt to different
environments. By plugging a novel loss estimator into the optimization problem that …

Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set
defined on a metric space, where the reward function is subject to a Lipschitz constraint. In …

Stochastic linear contextual bandit algorithms have substantial applications in practice, such
as recommender systems, online advertising, clinical trials, etc. Recent works show that …

In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given
dataset. We study reward-poisoning attacks in this setting where an exogenous attacker …

We study a federated variant of the best-arm identification problem in stochastic multi-armed
bandits: a set of clients, each of whom can sample only a subset of the arms, collaborate via …