Xiao Zhang | Security Research Group

Cost-Sensitive Adversarial Robustness at ICLR 2019

6 May 2019 adversarial machine learning, Xiao Zhang, ICLR

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]

Empirically Measuring Concentration

5 May 2019 adversarial machine learning, Xiao Zhang, ICLR, Saeed Mahloujifar, Mohammad Mahmoody

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.

Paper: [PDF]

ICLR 2019: Cost-Sensitive Robustness against Adversarial Examples

20 December 2018 adversarial machine learning, Xiao Zhang, ICLR

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.