49 citations · 141 across the 12 of their papers we have counts for
7 papers · 1 filter
Watermarking for Out-of-distribution Detection
Qizhou Wang, Feng Liu, Yonggang Zhang +4
Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. However, existing methods largely ignore the reprogr…
Towards Lightweight Black-Box Attacks against Deep Neural Networks
Chenghao Sun, Yonggang Zhang, Wan Chaoqun +5
Black-box attacks can generate adversarial examples without accessing the parameters of target model, largely exacerbating the threats of deployed deep neural networks (DNNs). Howe…
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi +3
In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a…
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
Yongqiang Chen, Kaiwen Zhou, Yatao Bian +8
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancin…
Counterfactual Fairness with Partially Known Causal Graph
Aoqi Zuo, Susan Wei, Tongliang Liu +3
Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair mach…
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang +4
Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of m…
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian +6
Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.g., images), studies on graph data are still limited. Dif…