# VC Classes are Adversarially Robustly Learnable, but Only Improperly

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

Cited by: 30|Views90
EI

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

Code:

Data:

Full Text
Bibtex
Your rating :
0

Best Paper
Best Paper of COLT, 2019
Tags
Comments