Poster
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser · Surbhi Goel · Ilias Diakonikolas · Nati Srebro
Keywords: [ Statistical Learning Theory ] [ Learning Theory ] [ Computational Learning Theory ] [ Adversarial Examples ]
Abstract:
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions on the adversarial perturbation sets under which halfspaces are efficiently robustly learnable. In the presence of random label noise, we give a simple computationally efficient algorithm for this problem with respect to any $\ell_p$-perturbation.
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