4 papers
S2O: Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has emerged as a highly effective way to improve the robustness of deep neural networks (DNNs). It is typically conceptualized as a min-max optimization proble…
Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifier
Gaojie Jin, Xinping Yi, Xiaowei Huang
Within the PAC-Bayesian framework, the Gibbs classifier (defined on a posterior ) and the corresponding -weighted majority vote classifier are commonly used to analyze the ge…
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
Gaojie Jin, Ronghui Mu, Xinping Yi +2
The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple en…
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
Ganlin Liu, Ziling Liang, Xiaowei Huang +2
Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phase…