no code implementations • 22 Apr 2025 • Guanchen Wu, Linzhi Zheng, Han Xie, Zhen Xiang, Jiaying Lu, Darren Liu, Delgersuren Bold, Bo Li, Xiao Hu, Carl Yang
By fine-tuning LLMs locally with synthetic notes, LPPA ensures strong privacy protection and high PHI annotation accuracy.
no code implementations • 14 Feb 2025 • Xinpeng Wang, Rong Zhou, Han Xie, Xiaoying Tang, Lifang He, Carl Yang
Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness.
no code implementations • 1 Feb 2025 • Karish Grover, Haiyang Yu, Xiang Song, Qi Zhu, Han Xie, Vassilis N. Ioannidis, Christos Faloutsos
Can integrating spectral and curvature signals unlock new potential in graph representation learning?
no code implementations • 25 Jan 2025 • Zhikai Chen, Han Xie, Jian Zhang, Xiang Song, Jiliang Tang, Huzefa Rangwala, George Karypis
The absence of dedicated datasets to formalize and evaluate the effectiveness of graph construction methods, and 2.
no code implementations • 22 Jan 2025 • Emir Ceyani, Han Xie, Baturalp Buyukates, Carl Yang, Salman Avestimehr
Recently proposed personalized subgraph FL methods have become the de-facto standard for training personalized Graph Neural Networks (GNNs) in a federated manner while dealing with the missing links across clients' subgraphs due to privacy restrictions.
no code implementations • 13 Jun 2024 • Zhen Xiang, Linzhi Zheng, YanJie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl Yang, Dawn Song, Bo Li
We also show that GuardAgent is able to define novel functions in adaption to emergent LLM agents and guard requests, which underscores its strong generalization capabilities.
no code implementations • 5 Jun 2023 • Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N. Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Belinda Zeng, Trishul Chilimbi
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
1 code implementation • 19 Nov 2021 • Luojun Lin, Han Xie, Zhishu Sun, WeiJie Chen, Wenxi Liu, Yuanlong Yu, Lei Zhang
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
1 code implementation • NeurIPS 2021 • Han Xie, Jing Ma, Li Xiong, Carl Yang
Federated learning has emerged as an important paradigm for training machine learning models in different domains.
1 code implementation • 14 Apr 2021 • Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.