13 citations · 26 across the 5 of their papers we have counts for
6 papers · 1 filter
Understanding Intrinsic Robustness Using Label Uncertainty
Xiao Zhang, David Evans
A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studyi…
Improved Estimation of Concentration Under -Norm Distance Metrics Using Half Spaces
Jack Prescott, Xiao Zhang, David Evans
Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a d…
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang, Jinghui Chen, Quanquan Gu +1
Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability…
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu, Xiao Zhang, David Evans
Training machine learning models that are robust against adversarial inputs poses seemingly insurmountable challenges. To better understand adversarial robustness, we consider the…
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody +1
Many recent works have shown that adversarial examples that fool classifiers can be found by minimally perturbing a normal input. Recent theoretical results, starting with Gilmer e…
Cost-Sensitive Robustness against Adversarial Examples
Xiao Zhang, David Evans
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the a…