45 citations · 91 across the 7 of their papers we have counts for
7 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…
Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning
Shuyang Yu, Junyuan Hong, Yi Zeng +3
Federated learning (FL) emerges as an effective collaborative learning framework to coordinate data and computation resources from massive and distributed clients in training. Such…
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…