4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
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)…