158 citations · 236 across the 7 of their papers we have counts for
17 papers
Learning Sample Reweighting for Accuracy and Adversarial Robustness
Chester Holtz, Tsui-Wei Weng, Gal Mishne
There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing th…
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Ren Wang, Kaidi Xu, Sijia Liu +4
Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization} of model…
Fast Training of Provably Robust Neural Networks by SingleProp
Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu +3
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally cos…
Higher-Order Certification for Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng +3
Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved SOTA provable robustness against perturbations. A number of publications…
Hidden Cost of Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei +4
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public. While immense interests were in either crafting adve…
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Jeet Mohapatra, Tsui-Wei, Weng +3
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the -norm t…