2 citations · 3 across the 5 of their papers we have counts for
5 papers
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
Omar Montasser, Han Shao, Emmanuel Abbe
Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in statistical le…
Strategic Classification under Unknown Personalized Manipulation
Han Shao, Avrim Blum, Omar Montasser
We study the fundamental mistake bound and sample complexity in the strategic classification, where agents can strategically manipulate their feature vector up to an extent in orde…
Agnostic Multi-Robust Learning Using ERM
Saba Ahmadi, Avrim Blum, Omar Montasser +1
A fundamental problem in robust learning is asymmetry: a learner needs to correctly classify every one of exponentially-many perturbations that an adversary might make to a test-ti…
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Avrim Blum, Omar Montasser, Greg Shakhnarovich +1
We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversariall…
A Theory of PAC Learnability under Transformation Invariances
Han Shao, Omar Montasser, Avrim Blum
Transformation invariances are present in many real-world problems. For example, image classification is usually invariant to rotation and color transformation: a rotated car in a…