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Reducing Adversarially Robust Learning to Non-Robust PAC Learning by Montasser, Omar; Hanneke, Steve; Srebro, Nathan is a scholarly article available to read on EtoBox.

What is Reducing Adversarially Robust Learning to Non-Robust PAC Learning about?

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.

Author

Montasser, Omar; Hanneke, Steve; Srebro, Nathan

Published

2020

Language

EN