Lune

ICML2025Top-tier venue

Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation

Wei Chen, Shigui Li, Jiacheng Li, Junmei Yang, John Paisley, Delu Zeng

2025Year
10Top-tier citations

Abstract

Density ratio estimation is fundamental to tasks involving f -divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping supports -the density-chasm and the support-chasm problems. Additionally, prior approaches yield divergent time scores near boundaries, leading to instability. We design D 3 RE, a unified framework for robust, stable and efficient density ratio estimation. We propose the dequantified diffusion bridge interpolant (DDBI), which expands support coverage and stabilizes time scores via diffusion bridges and Gaussian dequantization. Building on DDBI, the proposed dequantified Schrödinger bridge interpolant (DSBI) incorporates optimal transport to solve the Schrödinger bridge problem, enhancing accuracy and efficiency. Our method offers uniform approximation and bounded time scores in theory, and outperforms baselines empirically in mutual information and density estimation tasks. Code is available at https://github.com/Hoemr/Dequantified-Diffusion-Bridge-Density-Ratio-Estimation.git .

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext 16881f47-3d2a-4cf7-8fd7-0e2975de7498

Cited by top-tier papers10

Ask how each one uses it

Builds on10

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines