13 citations · 23 across the 10 of their papers we have counts for
7 papers · 1 filter
RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks
Xingran Chen, Rohit Bhagat, Ghadir Ayache +3
Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large foundation models. Adopting fine-tuning to distributed settings faces severa…
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
Tongnian Wang, Carolina Vivas-Valencia, Cici Bauer +3
Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify pa…
Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies
Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source c…
Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening
Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging r…
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Yifan Gao, Yanmin Gong, Yun Shi +1
Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care. Wearable sensors such as…
ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
Chenlang Yi, Gang Li, Zizhan Xiong +4
Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy and faithful, human-underst…
FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models
Guohao Yang, Tongle Wu, Yuanxiong Guo +2
Fine-tuning is essential to adapt general-purpose large language models (LLMs) to domain-specific tasks. As a privacy-preserving framework to leverage decentralized data for collab…