A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability

arXiv:2202.05420v3 Announce Type: replace
Abstract: We study the problem of learning an adversarially robust predictor to test time attacks in the semi-supervised PAC model. We address the question of how many labeled and unlabeled examples are required to ensure learning. We show that having enough unlabeled data (the size of a labeled sample that a fully-supervised method would require), the labeled sample complexity can be arbitrarily smaller compared to previous works, and is sharply characterized by a different complexity measure. …

abstract arxiv attacks cs.lg data examples question robust sample semi semi-supervised show stat.ml study test type