43 citations · 55 across the 8 of their papers we have counts for
9 papers · 1 filter
Theoretical Foundations of Adversarially Robust Learning
Omar Montasser
Despite extraordinary progress, current machine learning systems have been shown to be brittle against adversarial examples: seemingly innocuous but carefully crafted perturbations…
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
Omar Montasser, Steve Hanneke, Nathan Srebro
We present a minimax optimal learner for the problem of learning predictors robust to adversarial examples at test-time. Interestingly, we find that this requires new algorithmic i…
Transductive Robust Learning Guarantees
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of adversarially robust learning in the transductive setting. For classes of bounded VC dimension, we propose a simple transductive learner that…
Adversarially Robust Learning with Unknown Perturbation Sets
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of learning predictors that are robust to adversarial examples with respect to an unknown perturbation set, relying instead on interaction with an adversarial…
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.…