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arXiv · 2010.12039

Reducing Adversarially Robust Learning to Non-Robust PAC Learning

Omar Montasser·Steve Hanneke·Nathan Srebro
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Abstract

We study the problem of reducing adversarially robust learning to standard PAC learning, i.e. the complexity of learning adversarially robust predictors using access to only a black-box non-robust learner. We give a reduction that can robustly learn any hypothesis class $\mathcal{C}$ using any non-robust learner $\mathcal{A}$ for $\mathcal{C}$. The number of calls to $\mathcal{A}$ depends logarithmically on the number of allowed adversarial perturbations per example, and we give a lower bound showing this is unavoidable.

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Omar Montasser, Steve Hanneke, Nathan Srebro. 2020-10-22. Reducing Adversarially Robust Learning to Non-Robust PAC Learning. https://arxiv.org/abs/2010.12039

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