Statistics
Time: 2.00 p.m.
Date: 24th November 2025
Room 3001

Antonio OCELLO (Ecole Polytechnique) “Convergence Analysis of Diffusion Models: Towards Reliable Sampling”

Abstract :

Generative models are attracting growing attention across various applied domains, including insurance and finance. Their potential lies in their capacity to capture and reproduce complex data patterns, crucial for realistic modeling and decision-making under uncertainty. Among the available methods, Score-Based Generative Models (SGMs), also known as diffusion models, offer a flexible framework to sample from complex, high-dimensional distributions. However, a key challenge lies in rigorously understanding their convergence.

In this talk, I will present recent advances in the theoretical analysis of SGMs. First, I will provide explicit bounds on Wasserstein-2 distance under the log-concave assumption of the target data distribution. Second, I will generalize this bound beyond the log-concave settings—such as for mixtures of Gaussians.

This talk is based on joint work with Stanislas Strasman, Claire Boyer, Sylvain Le Corff, and Vincent Lemaire (TMLR 2024 – https://openreview.net/forum?id=BlYIPa0Fx1), as well as a recent collaboration with Marta Gentiloni-Silveri (ICML 2025 – https://arxiv.org/pdf/2501.02298).

Organizers:  Jaouad MOURTADA, Anna KORBA, Vincent DIVOL