15 citations · 31 across the 6 of their papers we have counts for
9 papers
Improved techniques for deterministic l2 robustness
Sahil Singla, Soheil Feizi
Training convolutional neural networks (CNNs) with a strict 1-Lipschitz constraint under the norm is useful for adversarial robustness, interpretable gradients and stable t…
Core Risk Minimization using Salient ImageNet
Sahil Singla, Mazda Moayeri, Soheil Feizi
Deep neural networks can be unreliable in the real world especially when they heavily use spurious features for their predictions. Recently, Singla & Feizi (2022) introduced the Sa…
Skew Orthogonal Convolutions
Sahil Singla, Soheil Feizi
Training convolutional neural networks with a Lipschitz constraint under the norm is useful for provable adversarial robustness, interpretable gradients, stable training, e…
Understanding Failures of Deep Networks via Robust Feature Extraction
Sahil Singla, Besmira Nushi, Shital Shah +2
Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over fea…
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Vedant Nanda, Samuel Dooley, Sahil Singla +2
Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these…
Second-Order Provable Defenses against Adversarial Attacks
Sahil Singla, Soheil Feizi
A robustness certificate is the minimum distance of a given input to the decision boundary of the classifier (or its lower bound). For {\it any} input perturbations with a magnitud…