2 citations · 3 across the 5 of their papers we have counts for
5 papers
EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems
Jun Hou, Priya Pitre, Yi Fang +1
Large language model (LLM) agent failures often contain multiple related errors rather than a single mistake. Existing attribution methods usually identify a responsible agent, ste…
Role-Specialized Mixture-of-Agents with Open-Weight LLMs for Clinical Prediction
Jun Hou, Yi Fang, Xuan Wang
Large Language Models (LLMs) are increasingly applied to clinical prediction tasks such as in-hospital mortality and readmission from electronic health records (EHRs). Privacy and…
BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration
Jun Hou, Le Wang, Xuan Wang
Mixture-of-Experts (MoE) models have become increasingly powerful in multimodal learning by enabling modular specialization across modalities. However, their effectiveness remains…
Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences
Jun Hou, Lucy Lu Wang
Explainable AI (XAI) techniques are necessary to help clinicians make sense of AI predictions and integrate predictions into their decision-making workflow. In this work, we conduc…
A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
Manar Aljohani, Jun Hou, Sindhura Kommu +1
The application of large language models (LLMs) in healthcare holds significant promise for enhancing clinical decision-making, medical research, and patient care. However, their i…