Reinforcement learning (RL) provides a theoretical framework for continuously improving an
agent's behavior via trial and error. However, efficiently learning policies from scratch can be …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

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 …

We introduce the problem of model selection for contextual bandits, where a learner must
adapt to the complexity of the optimal policy while balancing exploration and exploitation …

Decision-making in personalized medicine such as cancer therapy or critical care must often
make choices for dosage combinations, ie, multiple continuous treatments. Existing work for …

Designing efficient general-purpose contextual bandit algorithms that work with large—or
even infinite—action spaces would facilitate application to important scenarios such as …

Diffusion models excel at modeling complex data distributions, including those of images,
proteins, and small molecules. However, in many cases, our goal is to model parts of the …

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 …

We develop a generic data-driven method for estimator selection in off-policy policy
evaluation settings. We establish a strong performance guarantee for the method, showing …

Key challenges in running a retail business include how to select products to present to
consumers (the assortment problem), and how to price products (the pricing problem) to …