VC Classes are Adversarially Robustly Learnable, but Only Improperly
Machine Learning
2019-07-04 v2 Machine Learning
Abstract
We study the question of learning an adversarially robust predictor. We show that any hypothesis class with finite VC dimension is robustly PAC learnable with an improper learning rule. The requirement of being improper is necessary as we exhibit examples of hypothesis classes with finite VC dimension that are not robustly PAC learnable with any proper learning rule.
Keywords
Cite
@article{arxiv.1902.04217,
title = {VC Classes are Adversarially Robustly Learnable, but Only Improperly},
author = {Omar Montasser and Steve Hanneke and Nathan Srebro},
journal= {arXiv preprint arXiv:1902.04217},
year = {2019}
}
Comments
COLT 2019 Camera Ready