The Tractability Landscape of Sampling with Inexact Scores

arXiv:2607.19004 · stat.ML, cs.LG, math.ST · Submitted 2026-07-21 · Read on arXiv

Anming Gu, Kevin Tian, Hubert Yang, Yusong Zhu

stat.ML, cs.LG, math.ST

Submitted: 2026-07-21

Comments: 5 pages, 1 figure

License: http://creativecommons.org/licenses/by/4.0/

The gist: We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved target family.

Terminology

• Langevin Dynamics — 2× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 10 episodes
• Diffusion Models — 1× in this paper · explained in 9 episodes
• Editor — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Score Function — 1× in this paper · explained in 4 episodes

Abstract

Sources

• Convergence of the Inexact Langevin Algorithm in KL Divergence with Application to Score-based Generative Models

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• Scalable Posterior Uncertainty for Flexible Density-Based Clustering
• A Transport Geometry of Benign Overfitting
• Efficient quadratic entropy with distance sketches