13 citations · 32 across the 7 of their papers we have counts for
5 papers · 1 filter
CARE: Certifiably Robust Learning with Reasoning via Variational Inference
Jiawei Zhang, Linyi Li, Ce Zhang +1
Despite great recent advances achieved by deep neural networks (DNNs), they are often vulnerable to adversarial attacks. Intensive research efforts have been made to improve the ro…
Double Sampling Randomized Smoothing
Linyi Li, Jiawei Zhang, Tao Xie +1
Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and thus there is a line of work aiming to provide robustness certification for NNs, such as ran…
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
Jiawei Zhang, Linyi Li, Huichen Li +3
Boundary based blackbox attack has been recognized as practical and effective, given that an attacker only needs to access the final model prediction. However, the query efficiency…
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
Xinyu Peng, Jiawei Zhang, Fei-Yue Wang +1
Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The wid…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang +8
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification m…