2 citations · 3 across the 15 of their papers we have counts for
8 papers · 1 filter
Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
Songwei Dong, Zihan Chen, Chengshuai Shi +3
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluation…
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Xingbo Fu, Zhenyu Lei, Zihan Chen +3
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powe…
Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
Yuhan Yang, Xingbo Fu, Jundong Li
Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often remains between pre-training obj…
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Zihan Chen, Xingbo Fu, Yushun Dong +2
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods us…
Edge Prompt Tuning for Graph Neural Networks
Xingbo Fu, Yinhan He, Jundong Li
Pre-training powerful Graph Neural Networks (GNNs) with unlabeled graph data in a self-supervised manner has emerged as a prominent technique in recent years. However, inevitable o…
Verification of Machine Unlearning is Fragile
Binchi Zhang, Zihan Chen, Cong Shen +1
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, foll…
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
Zihan Chen, Song Wang, Cong Shen +1
In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample…
Personalized Federated Learning with Attention-based Client Selection
Zihan Chen, Jundong Li, Cong Shen
Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collabo…