24 citations · 44 across the 35 of their papers we have counts for
6 papers · 1 filter
Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
Yang Qiu, Yixiong Zou, Jun Wang +3
Out-of-distribution generalization under distributional shifts remains a critical challenge for graph neural networks. Existing methods generally adopt the Invariant Risk Minimizat…
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
Shiwei Li, Xiandi Luo, Xing Tang +6
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method. In standard LoRA layers, one of the matrices, or , is initialized to zero, ensuring that…
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
Shiwei Li, Xiandi Luo, Haozhao Wang +6
To improve the training efficiency of federated learning (FL), previous research has employed low-rank decomposition techniques to reduce communication overhead. In this paper, we…
Masked Random Noise for Communication Efficient Federated Learning
Shiwei Li, Yingyi Cheng, Haozhao Wang +7
Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…
A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
Ziwen Zhao, Yixin Su, Yuhua Li +3
Graph self-supervised learning (SSL) is now a go-to method for pre-training graph foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the graph data…
Masked Graph Autoencoder with Non-discrete Bandwidths
Ziwen Zhao, Yuhua Li, Yixiong Zou +2
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…