4 papers
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais, Philippe Formont, Pablo Piantanida +1
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance levels comparable to those of the origina…
Tree-based variational inference for Poisson log-normal models
Alexandre Chaussard, Anna Bonnet, Elisabeth Gassiat +1
When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or…
Variational excess risk bound for general state space models
Élisabeth Gassiat, Sylvain Le Corff
In this paper, we consider variational autoencoders (VAE) for general state space models. We consider a backward factorization of the variational distributions to analyze the exces…
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals. This a…