Wasserstein Contraction of Coordinate Ascent Variational Inference

Fuente: arXiv
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Auteurs principaux: Caprio, Rocco, Corenflos, Adrien, Power, Sam
Format: Preprint
Publié: 2026
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author Caprio, Rocco
Corenflos, Adrien
Power, Sam
author_facet Caprio, Rocco
Corenflos, Adrien
Power, Sam
contents We study the contraction in Wasserstein distance of the coordinate ascent variational inference algorithm. This is shown to hold under a transport-information inequality at the fixed points and a functional smoothness condition. The results are general and sharp, allow for local convergence guarantees, hold for general smooth manifolds, and also in some non-smooth spaces. We consider applications to Bayesian Gaussian Mixture Models, and high-dimensional Bayesian Probit Regression, and Logistic Regression with Pólya-Gamma random variables (i.e. Jaakkola-Jordan's algorithm).
format Preprint
id arxiv_https___arxiv_org_abs_2605_30253
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Wasserstein Contraction of Coordinate Ascent Variational Inference
Caprio, Rocco
Corenflos, Adrien
Power, Sam
Machine Learning
Functional Analysis
Optimization and Control
Probability
Computation
We study the contraction in Wasserstein distance of the coordinate ascent variational inference algorithm. This is shown to hold under a transport-information inequality at the fixed points and a functional smoothness condition. The results are general and sharp, allow for local convergence guarantees, hold for general smooth manifolds, and also in some non-smooth spaces. We consider applications to Bayesian Gaussian Mixture Models, and high-dimensional Bayesian Probit Regression, and Logistic Regression with Pólya-Gamma random variables (i.e. Jaakkola-Jordan's algorithm).
title Wasserstein Contraction of Coordinate Ascent Variational Inference
topic Machine Learning
Functional Analysis
Optimization and Control
Probability
Computation
url https://arxiv.org/abs/2605.30253