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 …

We seek to understand what facilitates sample-efficient learning from historical datasets for
sequential decision-making, a problem that is popularly known as offline reinforcement …

This paper studies reward-agnostic exploration in reinforcement learning (RL)—a scenario
where the learner is unware of the reward functions during the exploration stage—and …

Policy learning utilizing observational data is pivotal across various domains, with the
objective of learning the optimal treatment assignment policy while adhering to specific …

We consider offline policy optimization (OPO) in contextual bandits, where one is given a
fixed dataset of logged interactions. While pessimistic regularizers are typically used to …

Aligning large language models (LLMs) with human preferences is critical to recent
advances in generative artificial intelligence. Reinforcement learning from human feedback …

We study the problem of offline policy optimization in stochastic contextual bandit problems,
where the goal is to learn a near-optimal policy based on a dataset of decision data …

We consider estimation of a linear functional of the treatment effect from adaptively collected
data. This problem finds a variety of applications including off-policy evaluation in contextual …

While there now exists a large literature on policy evaluation and learning, much of prior
work assumes that the treatment assignment of one unit does not affect the outcome of …

We consider the problem of using observational bandit feedback data from multiple
heterogeneous data sources to learn a personalized decision policy that robustly …