Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

Reinforcement learning is a general technique that allows an agent to learn an optimal
policy and interact with an environment in sequential decision-making problems. The …

A treatment regime is a sequence of decision rules, one per decision point, that maps
accumulated patient information to a recommended intervention. An optimal treatment …

Off-policy evaluation learns a target policy's value with a historical dataset generated by a
different behavior policy. In addition to a point estimate, many applications would benefit …

Estimating dynamic treatment regimes (DTRs) from retrospective observational data is
challenging as some degree of unmeasured confounding is often expected. In this work, we …

Transfer learning for contextual multi-armed bandits Page 1 The Annals of Statistics 2024,
Vol. 52, No. 1, 207–232 https://doi.org/10.1214/23-AOS2341 © Institute of Mathematical …

The two-sided markets such as ride-sharing companies often involve a group of subjects
who are making sequential decisions across time and/or location. With the rapid …

We consider off-policy evaluation (OPE) in continuous treatment settings, such as
personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new …

We consider reinforcement learning (RL) methods in offline domains without additional
online data collection, such as mobile health applications. Most of existing policy …

Many modern tech companies, such as Google, Uber, and Didi, use online experiments
(also known as A/B testing) to evaluate new policies against existing ones. While most …