7 citations · 14 across the 10 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
Yuhan Li, Eugene Han, Yifan Hu +4
This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing r…
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework
Wenzhuo Zhou, Yuhan Li, Ruoqing Zhu +1
We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes, where the objective is to establish a confidence interval (CI) for the…
Stage-Aware Learning for Dynamic Treatments
Hanwen Ye, Wenzhuo Zhou, Ruoqing Zhu +1
Recent advances in dynamic treatment regimes (DTRs) facilitate the search for optimal treatments, which are tailored to individuals' specific needs and able to maximize their expec…
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Wenzhuo Zhou, Annie Qu, Keiland W. Cooper +2
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limi…
Quasi-optimal Reinforcement Learning with Continuous Actions
Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu
Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vi…