28 citations · 68 across the 11 of their papers we have counts for
3 papers · 1 filter
Smoothed Embeddings for Certified Few-Shot Learning
Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek +2
Randomized smoothing is considered to be the state-of-the-art provable defense against adversarial perturbations. However, it heavily exploits the fact that classifiers map input o…
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva +3
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Beside…
Robustness Threats of Differential Privacy
Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets
Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy in data analysis. It is well-known that the cost of adding DP to deep learning model is i…