2 citations · 3 across the 25 of their papers we have counts for
12 papers · 1 filter
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly r…
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Yilin Zhang, Cai Xu, Haishun Chen +2
Trusted multi-view classification typically relies on a view-wise evidential fusion process: each view independently produces class evidence and uncertainty, and the final predicti…
AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
Cai Xu, Changhao Sun, Ziyu Guan +1
Multi-view learning primarily aims to fuse multiple features to describe data comprehensively. Most prior studies implicitly assume that different views share similar dimensions. I…
Fairness-Aware Multi-view Evidential Learning with Adaptive Prior
Haishun Chen, Cai Xu, Jinlong Yu +5
Multi-view evidential learning aims to integrate information from multiple views to improve prediction performance and provide trustworthy uncertainty esitimation. Most previous me…
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Weigang Lu, Ziyu Guan, Wei Zhao +5
GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs).…
Discrepancy-Aware Graph Mask Auto-Encoder
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically re…
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Weigang Lu, Ziyu Guan, Wei Zhao +4
Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio in the image domain. Recently, the concept o…
Dynamic Evidence Decoupling for Trusted Multi-view Learning
Ying Liu, Lihong Liu, Cai Xu +3
Multi-view learning methods often focus on improving decision accuracy, while neglecting the decision uncertainty, limiting their suitability for safety-critical applications. To m…
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
Weigang Lu, Ziyu Guan, Wei Zhao +1
Graph Neural Networks (GNNs) have revolutionized graph-based machine learning, but their heavy computational demands pose challenges for latency-sensitive edge devices in practical…
Trusted Multi-view Learning under Noisy Supervision
Yilin Zhang, Cai Xu, Han Jiang +4
Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critica…
Reliable Conflictive Multi-View Learning
Cai Xu, Jiajun Si, Ziyu Guan +3
Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. Howev…
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
Weigang Lu, Ziyu Guan, Wei Zhao +2
Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled…