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cs.LG2021
Robust Adversarial Classification via Abstaining
Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
In this work, we consider a binary classification problem and cast it into a binary hypothesis testing framework, where the observations can be perturbed by an adversary. To improv…
cs.LG2020
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti
In this work we propose a graph-based learning framework to train models with provable robustness to adversarial perturbations. In contrast to regularization-based approaches, we f…