17 citations · 24 across the 16 of their papers we have counts for
7 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…
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…
Conformal Off-policy Prediction
Yingying Zhang, Chengchun Shi, Shikai Luo
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected…
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
Chengchun Shi, Shikai Luo, Yuan Le +2
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimizati…
Policy Evaluation for Temporal and/or Spatial Dependent Experiments
Shikai Luo, Ying Yang, Chengchun Shi +3
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial de…
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
Chengchun Shi, Jin Zhu, Ye Shen +3
This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most o…
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
Chengchun Shi, Runzhe Wan, Ge Song +3
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 development o…