1 citations · 1 across the 4 of their papers we have counts for
4 papers
Sparse Additive Off-Policy Evaluation for Reinforcement Learning with Potentially Limited Number of Trajectories
Tuoyi Zhao, Chengchun Shi, Zhengling Qi +1
We develop a new framework for flexible, nonlinear, and interpretable off-policy evaluation for infinite-horizon reinforcement learning. To handle large state spaces and support tr…
Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
Zeyu Bian, Lan Wang, Chengchun Shi +1
Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focu…
Off-policy Evaluation in Doubly Inhomogeneous Environments
Zeyu Bian, Chengchun Shi, Zhengling Qi +1
This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are bo…
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
Zuyue Fu, Zhengling Qi, Zhuoran Yang +2
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private informat…