5 papers
Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence properties remain only partially understood. In…
Exponential Convergence Guarantees for Iterative Markovian Fitting
Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
The Schrödinger Bridge (SB) problem has become a fundamental tool in computational optimal transport and generative modeling. To address this problem, ideal methods such as Iterati…
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance
Marta Gentiloni-Silveri, Antonio Ocello
Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for…
Theoretical guarantees in KL for Diffusion Flow Matching
Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
Flow Matching (FM) (also referred to as stochastic interpolants or rectified flows) stands out as a class of generative models that aims to bridge in finite time the target distrib…
KL Convergence Guarantees for Score diffusion models under minimal data assumptions
Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri
Diffusion models are a new class of generative models that revolve around the estimation of the score function associated with a stochastic differential equation. Subsequent to its…