Omar Montasser

VC Classes are Adversarially Robustly Learnable, but Only Improperly

2019 · arXiv (Cornell University)

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 …

Identifying unpredictable test examples with worst-case guarantees

2020

Often times, whether it be for adversarial or natural reasons, the distributions of test and training data differ. We give an algorithm that, given sets of training and test examples, identifies regions of test examples …