43 citations · 55 across the 8 of their papers we have counts for
4 papers · 1 filter
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of reducing adversarially robust learning to standard PAC learning, i.e. the complexity of learning adversarially robust predictors using access to only a blac…
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai +1
We present a transductive learning algorithm that takes as input training examples from a distribution and arbitrary (unlabeled) test examples, possibly chosen by an adversary.…
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas +1
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions…
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
Pritish Kamath, Omar Montasser, Nathan Srebro
We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather the…