89 citations · 170 across the 7 of their papers we have counts for
5 papers · 1 filter
GraphDefense: Towards Robust Graph Convolutional Networks
Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh
In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have sho…
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
Xuanqing Liu, Si Si, Xiaojin Zhu +2
In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL). In this framework, we first unify different tasks, goals,…
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Lu Wang, Xuanqing Liu, Jinfeng Yi +2
We study the problem of computing the minimum adversarial perturbation of the Nearest Neighbor (NN) classifiers. Previous attempts either conduct attacks on continuous approximatio…
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Xuanqing Liu, Tesi Xiao, Si Si +3
Neural Ordinary Differential Equation (Neural ODE) has been proposed as a continuous approximation to the ResNet architecture. Some commonly used regularization mechanisms in discr…
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Wei-Lin Chiang, Xuanqing Liu, Si Si +3
Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorit…