12/07/2020

Efficiently Learning Adversarially Robust Halfspaces with Noise

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nati Srebro

Keywords: Learning Theory

Abstract Paper Similar Papers

Abstract: We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. We give the first computationally efficient algorithm for this problem in the realizable setting and in the presence of random label noise with respect to any $\ell_p$-perturbation (and, more generally, perturbations with respect to any norm).

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