SOURCE-LINKED INTELLIGENCE

A Splitting Method for SDE Terminal-Law Estimation

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

In many settings involving stochastic differential equations, including in diffusion based generative AI, our aim is to accurately generate samples from a terminal distribution. Typically, this is done by generating i.i.d. samples of diffusion paths. Given a fixed simulation budget, a reasonable way to gain efficiency may be to instead generate a tree of paths through appropriately split partial paths. This suggests improved performance, but one worries about the injected dependence. In this paper, we study this issue comprehensively. With Kolmogorov-Smirnov distance as a measure of accuracy,

Read original source ↗ Open in workspace

recordType

paper

region

Global