2 papers
stat.ML2026
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Jianhan Zhang, Jitao Wang, John D. Piette +3
Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcar…
stat.ML2025
PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Jianhan Zhang, Jitao Wang, Chengchun Shi +3
Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment ac…