Emergent Mind
VC Classes are Adversarially Robustly Learnable, but Only Improperly
(1902.04217)
Published Feb 12, 2019
in
cs.LG
and
stat.ML
Abstract
We study the question of learning an adversarially robust predictor. We show that any hypothesis class $\mathcal{H}$ with finite VC dimension is robustly PAC learnable with an improper learning rule. The requirement of being improper is necessary as we exhibit examples of hypothesis classes $\mathcal{H}$ with finite VC dimension that are not robustly PAC learnable with any proper learning rule.
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