1 citations · 2 across the 8 of their papers we have counts for
8 papers
Towards Fair Predictions: Group Conditional Concordance Index to Quantify Fairness in Time-to-Event Prognostication
Haoyuan Wang, Riddhiman Bhattacharya, Richardo Henao +3
Fairness metrics are essential for rigorously defining, quantifying, and mitigating biases in predictive models. While most existing metrics focus on binary classification tasks, f…
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
Siqi Li, Chuan Hong, Ziye Tian +9
Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target…
RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty +3
Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce…
Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity
Siqi Li, Molei Liu, Yiwei Lyu +3
Machine learning models optimized for average performance can perform poorly on vulnerable subpopulations. Existing approaches often rely on groups specified in advance, yet fairne…
Model-X Change-Point Detection of Conditional Distribution
Zhuofan Dong, Yiwen Huang, Yan Dong +5
The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-p…
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
Wangxuan Fan, Siqi Li, Doudou Zhou +4
Explainable artificial intelligence (XAI) is essential for trustworthy machine learning (ML), particularly in high-stakes domains such as healthcare and finance. Shapley value (SV)…
Covariate Shift Corrected Conditional Randomization Test
Bowen Xu, Yiwen Huang, Chuan Hong +2
Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable from a treatment variable , conditioning on a se…
A Semiparametric Approach for Robust and Efficient Learning with Biobank Data
Molei Liu, Xinyi Wang, Chuan Hong
With the increasing availability of electronic health records (EHR) linked with biobank data for translational research, a critical step in realizing its potential is to accurately…