arXiv Machine Learning
Aug 14
Bagging Robustly Learns VC Classes with Linear Sample Complexity
arXiv:2608. 13514v1 Announce Type: cross Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time.
Bagging Robustly Learns VC Classes with Linear Sample Complexity
We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019).
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