17 citations · 71 across the 33 of their papers we have counts for
12 papers · 1 filter
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Yang Xu, Chengchun Shi, Shikai Luo +2
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of…
A Review of Off-Policy Evaluation in Reinforcement Learning
Masatoshi Uehara, Chengchun Shi, Nathan Kallus
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 paper…
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Yang Xu, Jin Zhu, Chengchun Shi +2
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior polic…
Doubly Inhomogeneous Reinforcement Learning
Liyuan Hu, Mengbing Li, Chengchun Shi +2
This paper studies reinforcement learning (RL) in doubly inhomogeneous environments under temporal non-stationarity and subject heterogeneity. In a number of applications, it is co…
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
Yunzhe Zhou, Zhengling Qi, Chengchun Shi +1
In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold…
Sure Screening for Transelliptical Graphical Models
Yuxiang Xie, Chengchun Shi, Rui Song
We propose a sure screening approach for recovering the structure of a transelliptical graphical model in the high dimensional setting. We estimate the partial correlation graph by…