885 citations · 1.7k across the 71 of their papers we have counts for
4 papers · 2 filters
Adversarially Robust Classification based on GLRT
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore…
Asymptotic Behavior of Adversarial Training in Binary Classification
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
It has been consistently reported that many machine learning models are susceptible to adversarial attacks i.e., small additive adversarial perturbations applied to data points can…
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
Empirical Risk Minimization (ERM) algorithms are widely used in a variety of estimation and prediction tasks in signal-processing and machine learning applications. Despite their p…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…