4 citations · 6 across the 13 of their papers we have counts for
15 papers
Human-Anchored Inference for Ranking New Models with Large Language Model Judges
Xin Zhou, Sinian Zhang, Zhanyan Yang +3
Human pairwise comparisons provide a reference for evaluating large language models (LLMs), but collecting sufficient judgments for each new release is costly and time-consuming. L…
Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
Longtian Shi, Molei Liu, Doudou Zhou
In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved. Evaluating its tar…
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…
Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes
Yuying Lu, Wenbo Fei, Yuanjia Wang +1
Precision mental health requires treatment decisions that account for heterogeneous symptoms reflecting multiple clinical domains. However, existing methods for estimating individu…
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI
Xin Xiong, Zijian Guo, Haobo Zhu +4
Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10)…
TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk
Mengying Yan, Ziye Tian, Siqi Li +4
Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical e…
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)…
Transfer Learning of CATE with Kernel Ridge Regression
Seok-Jin Kim, Hongjie Liu, Molei Liu +1
The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outco…
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Siqi Li, Xin Li, Kunyu Yu +12
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effecti…
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…
Robust and Efficient Semi-supervised Learning for Ising Model
Daiqing Wu, Molei Liu
In biomedical studies, it is often desirable to characterize the interactive mode of multiple disease outcomes beyond their marginal risk. Ising model is one of the most popular ch…