Efficiently Learning Adversarially Robust Halfspaces with Noise


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 ℓp-perturbation.

International Conference on Machine Learning (ICML) 2020
Surbhi Goel
Surbhi Goel
Assistant Professor