25 citations · 29 across the 5 of their papers we have counts for
4 papers · 1 filter
Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas +3
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fails to transfer to other attacks. While pr…
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan, Adam Ibrahim, Amirozhan Dehghani +1
Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations. Pseudo-ensemble meth…
Adversarial Feature Desensitization
Pouya Bashivan, Reza Bayat, Adam Ibrahim +5
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can drastically impair the network's perform…
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim, Waïss Azizian, Gauthier Gidel +1
Recent successes of game-theoretic formulations in ML have caused a resurgence of research interest in differentiable games. Overwhelmingly, that research focuses on methods and up…