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cs.LG2026

AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models

Yuanyun Zhang, Shi Li

Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent representations frequently entangle p…

cs.LG2026

WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records

Ruan Dong, Yuanyun Zhang, Shi Li

Representation learning in electronic health records (EHR) has largely followed paradigms inherited from natural language processing, relying on sequence modeling and reconstructio…

cs.LG2026

Event Fields: Learning Latent Event Structure for Waveform Foundation Models

Li Na, Yuanyun Zhang, Shi Li

We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time series as realizations of latent e…

cs.LG2026

Uncertainty-Aware Foundation Models for Clinical Data

Qian Zhou, Yuanyun Zhang, Shi Li

Healthcare foundation models have largely followed paradigms from natural language processing and computer vision, emphasizing large scale pretraining and deterministic representat…

cs.LG2026

Learning Clinical Representations Under Systematic Distribution Shift

Yuanyun Zhang, Shi Li

Clinical machine learning models are increasingly trained using large scale, multimodal foundation paradigms, yet deployment environments often differ systematically from the data…

cs.LG2026

Dense Feature Learning via Linear Structure Preservation in Medical Data

Yuanyun Zhang, Mingxuan Zhang, Siyuan Li +4

Deep learning models for medical data are typically trained using task specific objectives that encourage representations to collapse onto a small number of discriminative directio…

cs.LG2026

Learning Longitudinal Health Representations from EHR and Wearable Data

Yuanyun Zhang, Han Zhou, Li Feng +2

Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable dev…

cs.LG2025

ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling

Yuanyun Zhang, Shi Li

The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFo…