19 citations · 52 across the 9 of their papers we have counts for
6 papers · 1 filter
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni +4
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal re…
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Yulin Zhu, Liang Tong, Gaolei Li +2
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models. We present FocusedCleaner as a poisoned…
Towards Robustness against Unsuspicious Adversarial Examples
Liang Tong, Minzhe Guo, Atul Prakash +1
Despite the remarkable success of deep neural networks, significant concerns have emerged about their robustness to adversarial perturbations to inputs. While most attacks aim to e…
Defending Against Physically Realizable Attacks on Image Classification
Tong Wu, Liang Tong, Yevgeniy Vorobeychik
We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and e…
Adversarial Regression with Multiple Learners
Liang Tong, Sixie Yu, Scott Alfeld +1
Despite the considerable success enjoyed by machine learning techniques in practice, numerous studies demonstrated that many approaches are vulnerable to attacks. An important clas…