84 citations
- Laboratoire de Probabilités et Modèles AléatoiresFR194 papers
- Sorbonne UniversitéFR76 papers
- Université Paris CitéFR59 papers
- Centre National de la Recherche ScientifiqueFR52 papers
- Sorbonne Paris CitéFR35 papers
- Institut Universitaire de FranceFR20 papers
- Laboratoire de Mathématiques Blaise PascalFR20 papers
- Laboratoire de Mathématiques d'OrsayFR20 papers
- École PolytechniqueFR17 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR15 papers
- Université Paris-SaclayFR14 papers
- Département de mathématiques et applicationsFR12 papers
12 papers · 2 filters
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
Lilian Say, Christophe Denis, Rafael Pinot
The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-p…
Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1
Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…
Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen, Christophe Denis, Mohamed Hebiri
Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discrimi…
Optimal Stopping in Latent Diffusion Models
Yu-Han Wu, Quentin Berthet, Gérard Biau +3
We identify and analyze a surprising phenomenon of Latent Diffusion Models (LDMs) where the final steps of the diffusion can degrade sample quality. In contrast to conventional arg…
Fourier Analysis on the Boolean Hypercube via Hoeffding Functional Decomposition
Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +2
Fourier analysis on the Boolean hypercube is fundamentally defined as the orthogonal decomposition of the space of pseudo-Boolean functions with respect to the uniform probability…
Fast kernel methods: Sobolev, physics-informed, and additive models
Nathan Doumèche, Francis Bach, Gérard Biau +1
Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a sc…