21 citations · 42 across the 5 of their papers we have counts for
12 papers
Invariant Learning via Diffusion Dreamed Distribution Shifts
Priyatham Kattakinda, Alexander Levine, Soheil Feizi
Though the background is an important signal for image classification, over reliance on it can lead to incorrect predictions when spurious correlations between foreground and backg…
Provable Adversarial Robustness for Fractional Lp Threat Models
Alexander Levine, Soheil Feizi
In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks…
Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Randomized smoothing is a general technique for computing sample-dependent robustness guarantees against adversarial attacks for deep classifiers. Prior works on randomized smoothi…
Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi +1
Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top tw…
Tight Second-Order Certificates for Randomized Smoothing
Alexander Levine, Aounon Kumar, Thomas Goldstein +1
Randomized smoothing is a popular way of providing robustness guarantees against adversarial attacks: randomly-smoothed functions have a universal Lipschitz-like bound, allowing fo…
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Wei-An Lin, Chun Pong Lau, Alexander Levine +2
Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the def…