1 citations · 1 across the 5 of their papers we have counts for
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Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
Wentao Yu, Sheng Wan, Shuo Chen +2
Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from di…
LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics
Jialiang Tang, Shuo Chen, Chen Gong +2
Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs),…
Modeling Inter-Intra Heterogeneity for Graph Federated Learning
Wentao Yu, Shuo Chen, Yongxin Tong +2
Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the h…
Robust Learning under Hybrid Noise
Yang Wei, Shuo Chen, Shanshan Ye +2
Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal…
Atom-Motif Contrastive Transformer for Molecular Property Prediction
Wentao Yu, Shuo Chen, Chen Gong +2
Recently, Graph Transformer (GT) models have been widely used in the task of Molecular Property Prediction (MPP) due to their high reliability in characterizing the latent relation…