Author

Benjamin Dupuis

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Jul 2026

Tightening the Score Matching Gap for Diffusion Models

This work develops tighter bounds for three metrics: KL divergence, reverse KL divergence, and Wasserstein distance, effectively exploiting the regularity of the class of score estimators, and suggests that the quality of the score approximation has more impact on closing the score matching gap for low noise scales.

Benjamin Dupuis, Tyler Farghly, Maxime Haddouche et al. · arXiv.org · 0 citations

#machine learning Preprint Feb 2025

Generalization Bounds for Markov Algorithms through Entropy Flow Computations

This work builds on a principled continuous-time approximation of Markov algorithms and introduces a new, exact entropy flow formula for such processes, and establishes novel connections to a well-studied family of modified logarithmic Sobolev inequalities.

Benjamin Dupuis, Maxime Haddouche, George Deligiannidis et al. · 2 citations