Review for NeurIPS paper: Reducing Adversarially Robust Learning to Non-Robust PAC Learning

Summary and Contributions: Robust learning has got a lot of attention over the last decade. This paper is about *test-time attacks (called evasion attacks or attacks finding "adversarial examples"). Such attacks are studied widely from experimental point of view, but more recently theoretical results are being proven for understanding how, and when, learning under such adversarial perturbations are possible. Montasser et al (MHS COLT 19) showed that if a problem is PAC learnable, i.e., has finite VC dimension d, then it is also robustly PAC learnable, though the sample complexity (in their proof) could be as large as exponential(d). This paper's main contribution is an alternative proof of the result of MHS, by showing how to reduce the task of robust learning to the task of normal PAC learning.