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arXiv · 1902.04217

VC Classes are Adversarially Robustly Learnable, but Only Improperly

Omar Montasser·Steve Hanneke·Nathan Srebro
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Abstract

We study the question of learning an adversarially robust predictor. We show that any hypothesis class $\mathcal{H}$ 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 $\mathcal{H}$ with finite VC dimension that are not robustly PAC learnable with any proper learning rule.

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Omar Montasser, Steve Hanneke, Nathan Srebro. 2019-02-12. VC Classes are Adversarially Robustly Learnable, but Only Improperly. https://arxiv.org/abs/1902.04217

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