2 citations · 7 across the 17 of their papers we have counts for
6 papers · 1 filter
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Farica Zhuang, Seong Woo Han, Zixuan Wen +3
Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains…
Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routing
Yichi Zhang, Fangzheng Xie, Shu Yang +1
In language tasks that require extensive human--model interaction, deploying a single "best" model for every query can be expensive. To reduce inference cost while preserving the q…
ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning
Ruochen Jin, Boning Tong, Shu Yang +2
Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a…
MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
Jia Xu, Tianyi Wei, Bojian Hou +7
We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between B…
Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion
Tianyi Wei, Shu Yang, Davoud Ataee Tarzanagh +6
Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose…
Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models
Joseph Lee, Shu Yang, Jae Young Baik +8
Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are util…