3 papers
cs.LG2026
Adversarial Samples Are Not Created Equal
Jennifer Crawford, Amol Khanna, Fred Lu +4
Over the past decade, numerous theories have been proposed to explain the widespread vulnerability of deep neural networks to adversarial evasion attacks. Among these, the theory o…
cs.CV2025
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent
Philip Doldo, Derek Everett, Amol Khanna +2
Projected Gradient Descent (PGD) under the ball has become one of the defacto methods used in adversarial robustness evaluation for computer vision (CV) due to its relia…
cs.CR2024
Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
Amol Khanna, Adam McCormick, Andre Nguyen +2
In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researc…