6 citations · 10 across the 13 of their papers we have counts for
15 papers · 1 filter
Performative Privacy: When Differential Privacy Maximizes Utility
Uddalak Mukherjee, Edwige Cyffers, Yann Chevaleyre
Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in the long term. However, this…
Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case
David A. R. Robin, Rafael Pinot, Yann Chevaleyre
The vulnerability of ML models to adversarial examples has recently emerged as a major concern. While adversarial training is one of the most effective countermeasures to this issu…
Lattice Climber Attack: Adversarial attacks for randomized mixtures of classifiers
Lucas Gnecco-Heredia, Benjamin Negrevergne, Yann Chevaleyre
Finite mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, existing attacks have been show…
Improving Discriminator Guidance in Diffusion Models
Alexandre Verine, Ahmed Mehdi Inane, Florian Le Bronnec +2
Discriminator Guidance has become a popular method for efficiently refining pre-trained Score-Matching Diffusion models. However, in this paper, we demonstrate that the standard im…
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
Lucas Gnecco-Heredia, Matteo Sammut, Muni Sreenivas Pydi +3
Randomization as a mean to improve the adversarial robustness of machine learning models has recently attracted significant attention. Unfortunately, much of the theoretical analys…
Optimal Budgeted Rejection Sampling for Generative Models
Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne +1
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models. However, these methods are only optimal under an unlimit…