Author

Alain Durmus

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#machine learning Jun 2026

Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

This work provides refined and novel convergence guarantees for Brownian motion based DFMs, focusing on the discretization error, and derives KL convergence bounds with improved dimensional dependence compared to prior work, achieving, up to their knowledge, state-of-the-art scaling under minimal conditions.

Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus · arXiv.org · 1 citation

Preprint Aug 2026

Trajectory inference via Acceleration Matching

This work proposes a new algorithm called Acceleration Matching, which consists of lifting the original interpolation problem to phase space and then regressing onto an explicit conditional acceleration field that induces random, smooth trajectories that agree with the prescribed marginals.

Bartolo Dazzini, Giovanni Conforti, Alain Durmus et al. · 1 citation