9 papers
Trajectory inference via Acceleration Matching
Bartolo Dazzini, Giovanni Conforti, Alain Durmus +1
Trajectory inference is a fundamental problem in many scientific domains: given a collection of unpaired snapshots of observations at discrete time points, the goal is to generate…
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…
Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics
Giovanni Conforti, Alain Durmus, Le-Tuyet-Nhi Pham +1
Diffusion models for continuous state spaces based on Gaussian noising processes are now relatively well understood from both practical and theoretical perspectives. In contrast, r…
Iterative Tilting for Diffusion Fine-Tuning
Jean Pachebat, Giovanni Conforti, Alain Durmus +1
We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt 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 Iterat…
Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions
Le-Tuyet-Nhi Pham, Dario Shariatian, Antonio Ocello +2
This paper introduces Discrete Markov Probabilistic Models (DMPMs), a novel discrete diffusion algorithm for discrete data generation. The algorithm operates in discrete bit space,…