6 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…
Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights
Zeyu Bian, Max Biggs, Ruijiang Gao +1
This paper develops a practical framework for using observational data to audit the consumer surplus effects of AI-driven decisions, specifically in targeted pricing and algorithmi…
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
Shuguang Yu, Shuxing Fang, Ruixin Peng +3
This paper studies off-policy evaluation (OPE) in the presence of unmeasured confounders. Inspired by the two-way fixed effects regression model widely used in the panel data liter…
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi +2
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applicati…
Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments
Jiayi Wang, Zhengling Qi, Chengchun Shi
As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an im…