VC Classes are Adversarially Robustly Learnable, but Only Improperly

    Omar Montasser
    Omar Montasser

    conference on learning theory, pp. 2512-2530, 2019.

    Cited by: 24|Bibtex|Views31|Links
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    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 ar...More

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