We consider the general (stochastic) contextual bandit problem under the realizability
assumption, that is, the expected reward, as a function of contexts and actions, belongs to a …

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 …

In many applications, eg in healthcare and e-commerce, the goal of a contextual bandit may
be to learn an optimal treatment assignment policy at the end of the experiment. That is, to …

How to explore efficiently is a central problem in multi-armed bandits. In this paper, we
introduce the metadata-based multi-task bandit problem, where the agent needs to solve a …

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear
function approximations. We propose to study convergence to approximate local maxima …

We design and implement an adaptive experiment (a``contextual bandit'') to learn a targeted
treatment assignment policy, where the goal is to use a participant's survey responses to …

We consider the problem of combining and learning over a set of adversarial bandit
algorithms with the goal of adaptively tracking the best one on the fly. The Corral algorithm of …

Contextual bandit algorithms often estimate reward models to inform decision-making.
However, true rewards can contain action-independent redundancies that are not relevant …

Much of the literature on optimal design of bandit algorithms is based on minimization of
expected regret. It is well known that algorithms that are optimal over certain exponential …

This paper proposes a linear bandit algorithm that is adaptive to environments at two
different levels of hierarchy. At the higher level, the proposed algorithm adapts to a variety of …