45 citations · 144 across the 11 of their papers we have counts for
11 papers
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
Haobo Zhang, Jiankun Wang, Suraj Rajendran +5
Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adap…
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Suraj Rajendran, Weishen Pan, Mert R. Sabuncu +3
Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clin…
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
Qianqian Xie, Jiayu Zhou, Yifan Peng +1
Automatic radiology report summarization is a crucial clinical task, whose key challenge is to maintain factual accuracy between produced summaries and ground truth radiology findi…
Persona Authentication through Generative Dialogue
Fengyi Tang, Lifan Zeng, Fei Wang +1
In this paper we define and investigate the problem of \emph{persona authentication}: learning a conversational policy to verify the consistency of persona models. We propose a lea…
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge +2
In recent years, increasingly augmentation of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportuni…
Boosted Sparse and Low-Rank Tensor Regression
Lifang He, Kun Chen, Wanwan Xu +2
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coeffic…
Model-Protected Multi-Task Learning
Jian Liang, Ziqi Liu, Jiayu Zhou +3
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independent…
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Xi Sheryl Zhang, Lifang He, Kun Chen +3
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few…
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Tengfei Ma, Cao Xiao, Jiayu Zhou +1
Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properti…
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
Mengying Sun, Fengyi Tang, Jinfeng Yi +2
The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling. Recently many deep learning based predicted model…
Asynchronous Multi-Task Learning
Inci M. Baytas, Ming Yan, Anil K. Jain +1
Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a ce…