KL Convergence Guarantees for Score diffusion models under minimal data assumptions

Name: KL Convergence Guarantees for Score diffusion models under minimal data assumptions
Uploaded: 2026-10-06T10:47:09.830985+00:00
Duration: 280 s
Description: This work establishes sharp convergence bounds for score diffusion models, which simulate time-reversal processes to generate data samples. The authors provide explicit bounds on the Kullback-Leibler divergence between the true data distribution and the model's approximation, requiring only finite Fisher information rather than strong assumptions like score function smoothness.

This work establishes sharp convergence bounds for score diffusion models, which simulate time-reversal processes to generate data samples. The authors provide explicit bounds on the Kullback-Leibler divergence between the true data distribution and the model's approximation, requiring only finite Fisher information rather than strong assumptions like score function smoothness.

Open the full episode