Our paper on Improved Estimation of Concentration Under ℓp-Norm Distance Metrics Using Half Spaces (Jack Prescott, Xiao Zhang, and David Evans) will be presented at ICLR 2021.
Abstract: Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets.
Xiao Zhang will present Cost-Sensitive Robustness against Adversarial Examples on May 7 (4:30-6:30pm) at ICLR 2019 in New Orleans.
Paper: [PDF] [OpenReview] [ArXiv]
Xiao Zhang and Saeed Mahloujifar will present our work on Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness at two workshops May 6 at ICLR 2019 in New Orleans: Debugging Machine Learning Models and Safe Machine Learning:
Specification, Robustness and Assurance.
Xiao Zhang and my paper on Cost-Sensitive Robustness against Adversarial Examples has been accepted to ICLR 2019.
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier’s performance for specific tasks.