25 citations · 116 across the 31 of their papers we have counts for
34 papers
Frame the adversary: a structure-aware attack methodology
Vicky Kouni, Stelios Perrakis, Francis Bach +2
Frequency-based adversarial attacks have recently grown popular by exploiting spectral sensitivities shared across neural architectures. Unlike spatial perturbations, frequency-bas…
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…
MetagenBERT: a Transformer-based Architecture using Foundational genomic Large Language Models for novel Metagenome Representation
Gaspar Roy, Eugeni Belda, Baptiste Hennecart +3
Metagenomic disease prediction commonly relies on species abundance tables derived from large, incomplete reference catalogs, constraining resolution and discarding valuable inform…
Improving Diversity in Language Models: When Temperature Fails, Change the Loss
Alexandre Verine, Florian Le Bronnec, Kunhao Zheng +3
Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach…
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…