118 citations · 426 across the 23 of their papers we have counts for
9 papers · 1 filter
Provable Training for Graph Contrastive Learning
Yue Yu, Xiao Wang, Mengmei Zhang +2
Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximi…
Local Boosting for Weakly-Supervised Learning
Rongzhi Zhang, Yue Yu, Jiaming Shen +2
Boosting is a commonly used technique to enhance the performance of a set of base models by combining them into a strong ensemble model. Though widely adopted, boosting is typicall…
Domain Agnostic Fourier Neural Operators
Ning Liu, Siavash Jafarzadeh, Yue Yu
Fourier neural operators (FNOs) can learn highly nonlinear mappings between function spaces, and have recently become a popular tool for learning responses of complex physical syst…
Neighborhood-Regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui +5
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been succ…
EDoG: Adversarial Edge Detection For Graph Neural Networks
Xiaojun Xu, Yue Yu, Hanzhang Wang +3
Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are…
A Survey on Programmatic Weak Supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu +2
Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved…