1 citations · 2 across the 6 of their papers we have counts for
8 papers
Censored Flow Matching for Synthetic Time-to-Event Generation
Jianbin Tan, Xingran Chen, Kevin He +3
Generating time-to-event data is increasingly important in survival analysis, but it remains statistically challenging because event times are often censored and thus prevent flexi…
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…
Prediction-based Inference in Electronic Health Record (EHR)-linked Biobanks with Clinically Informative Outcomes
Xingran Chen, Cheng-Han Yang, Zhenke Wu +1
Electronic health record (EHR)-linked biobank data hold tremendous promise for large-scale discoveries via genome-wide association study (GWAS) on diverse phenotypic traits and bio…
Spatially Robust Inference with Predicted and Missing at Random Labels
Stephen Salerno, Zhenke Wu, Tyler McCormick
When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has c…
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…
Generalized Fitted Q-Iteration with Clustered Data
Liyuan Hu, Jitao Wang, Zhenke Wu +1
This paper focuses on reinforcement learning (RL) with clustered data, which is commonly encountered in healthcare applications. We propose a generalized fitted Q-iteration (FQI) a…
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
Jitao Wang, Chengchun Shi, John D. Piette +3
When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy m…
Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework
Lan Luo, Chengchun Shi, Jitao Wang +2
Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there a…