1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Verification of Neural Networks' Global Robustness
Anan Kabaha, Dana Drachsler-Cohen
Neural networks are successful in various applications but are also susceptible to adversarial attacks. To show the safety of network classifiers, many verifiers have been introduc…
cs.LG2023
Verification of Neural Networks Local Differential Classification Privacy
Roie Reshef, Anan Kabaha, Olga Seleznova +1
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy pro…
cs.PL2016
Optimal Learning of Specifications from Examples
Dana Drachsler-Cohen, Martin Vechev, Eran Yahav
A fundamental challenge in synthesis from examples is designing a learning algorithm that poses the minimal number of questions to an end user while guaranteeing that the target hy…