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Poster
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser · Surbhi Goel · Ilias Diakonikolas · Nati Srebro

Tue Jul 14 06:00 PM -- 06:45 PM & Wed Jul 15 04:00 AM -- 04:45 AM (PDT) @
in Poster Session 10 »
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.
Keywords: Adversarial Examples · Computational Learning Theory · Learning Theory · Statistical Learning Theory

Author Information

Omar Montasser (TTI-Chicago)
Surbhi Goel (University of Texas at Austin)
Ilias Diakonikolas (University of Wisconsin-Madison)
Nati Srebro (Toyota Technological Institute at Chicago)

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