7 citations · 14 across the 10 of their papers we have counts for
10 papers
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…
Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon
Wenzhuo Zhou, Yuhan Li, Ruoqing Zhu
With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve bot…
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…
Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning
Wenzhuo Zhou, Ruoqing Zhu, Annie Qu
Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However,…
Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data
Fei Xue, Yanqing Zhang, Wenzhuo Zhou +2
An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection…
orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization
Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao +3
orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regress…
A Parsimonious Personalized Dose Finding Model via Dimension Reduction
Wenzhuo Zhou, Ruoqing Zhu, Donglin Zeng
Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when…