Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Ferdinand Genans5 · h 1
- Olivier Wintenberger5 · h 19
- Sylvain Le Corff4 · h 4
- Stanislas Strasman2 profiles3 · h 2
- Antoine Godichon-Baggioni2 · h 2
- Antonio Ocello2 · h 3
- Claire Boyer2 profiles2 · h 6
- François-Xavier Vialard2 · h 25
- Jordan Serres2 · h 3
- Pierre Gaillard2 profiles2 · h 4
- Vincent Lemaire2 · h 2
- Alexandre Delplanque1 · h 1
- Laboratoire de Probabilités et Modèles AléatoiresFR9 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR9 papers
- Sorbonne UniversitéFR9 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR2 papers
- École PolytechniqueFR2 papers
- Institut Universitaire de FranceFR2 papers
- Laboratoire de Mathématiques d'OrsayFR2 papers
- Laboratoire d'Informatique Gaspard-MongeFR2 papers
- Université Paris-SaclayFR2 papers
- Centre Inria de SaclayFR1 paper
- Centre Inria d'Université Côte d'AzurFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
6 papers · 1 filter
Theoretical Guarantees for SMC-Guided Diffusion Sampling
Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2
Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion s…
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
Stanislas Strasman, Sobihan Surendran, Sylvain Le Corff
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling…
Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
Ferdinand Genans, Erwan Scornet
Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce syste…
Geometry-Aware Optimal Transport: Fast Intrinsic Dimension and Wasserstein Distance Estimation
Ferdinand Genans, Olivier Wintenberger
Solving large scale Optimal Transport (OT) in machine learning typically relies on sampling measures to obtain a tractable discrete problem. While the discrete solver's accuracy is…
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
Adding entropic regularization to Optimal Transport (OT) problems has become a standard approach for designing efficient and scalable solvers. However, regularization introduces a…
Attention-based clustering
Rodrigo Maulen-Soto, Pierre Marion, Claire Boyer
Transformers have emerged as a powerful neural network architecture capable of tackling a wide range of learning tasks. In this work, we provide a theoretical analysis of their abi…