584 citations · 805 across the 20 of their papers we have counts for
11 papers · 1 filter
Imbalanced Adversarial Training with Reweighting
Wentao Wang, Han Xu, Xiaorui Liu +3
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks. However, almost all existing studies abou…
Elastic Graph Neural Networks
Xiaorui Liu, Wei Jin, Yao Ma +5
While many existing graph neural networks (GNNs) have been proven to perform -based graph smoothing that enforces smoothness globally, in this work we aim to further enhanc…
Towards the Memorization Effect of Neural Networks in Adversarial Training
Han Xu, Xiaorui Liu, Wentao Wang +5
Recent studies suggest that ``memorization'' is one important factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly f…
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node cla…
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao +3
Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designe…
Graph Feature Gating Networks
Wei Jin, Xiaorui Liu, Yao Ma +3
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where…
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Han Xu, Xiaorui Liu, Yaxin Li +2
Adversarial training algorithms have been proved to be reliable to improve machine learning models' robustness against adversarial examples. However, we find that adversarial train…
A Unified View on Graph Neural Networks as Graph Signal Denoising
Yao Ma, Xiaorui Liu, Tong Zhao +3
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…
Linear Convergent Decentralized Optimization with Compression
Xiaorui Liu, Yao Li, Rongrong Wang +2
Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DG…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…
A Double Residual Compression Algorithm for Efficient Distributed Learning
Xiaorui Liu, Yao Li, Jiliang Tang +1
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchron…