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A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
May 7, 2024, 4:44 a.m. | Idan Attias, Steve Hanneke, Yishay Mansour
cs.LG updates on arXiv.org arxiv.org
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
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