5 papers
Robust Learning of Heterogeneous Dynamic Systems
Shuoxun Xu, Zijian Guo, Brooke R. Staveland +2
Ordinary differential equations (ODEs) provide a powerful framework for modeling dynamic systems arising in a wide range of scientific domains. However, most existing ODE methods f…
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)…
StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
Zhenyu Wang, Molei Liu, Jing Lei +2
When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across differ…
Multi-source Stable Variable Importance Measure via Adversarial Machine Learning
Zitao Wang, Nian Si, Zijian Guo +1
The quantification and inference of predictive importance for exposure covariates have recently gained significant attention in the context of interpretable machine learning. Conte…
Domain Adaptation Optimized for Robustness in Mixture Populations
Keyao Zhan, Xin Xiong, Zijian Guo +2
While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced b…