Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation

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Bibliographic Details
Main Authors:	Rozzio, Adam, Athanasiades, Rafael, Akyildiz, O. Deniz
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
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author	Rozzio, Adam Athanasiades, Rafael Akyildiz, O. Deniz
author_facet	Rozzio, Adam Athanasiades, Rafael Akyildiz, O. Deniz
contents	Maximum marginal likelihood estimation (MMLE) can be formulated as the optimization of a free energy functional. From this viewpoint, the Expectation-Maximisation (EM) algorithm admits a natural interpretation as a coordinate descent method over the joint space of model parameters and probability measures. Recently, a significant body of work has adopted this perspective, leading to interacting particle algorithms for MMLE. In this paper, we propose an accelerated version of one such procedure, based on Stein variational gradient descent (SVGD), by introducing Nesterov acceleration in both the parameter updates and in the space of probability measures. The resulting method, termed Momentum SVGD-EM, consistently accelerates convergence in terms of required iterations across various tasks of increasing difficulty, demonstrating effectiveness in both low- and high-dimensional settings.
format	Preprint
id	arxiv_https___arxiv_org_abs_2603_08676
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation Rozzio, Adam Athanasiades, Rafael Akyildiz, O. Deniz Machine Learning Computation Maximum marginal likelihood estimation (MMLE) can be formulated as the optimization of a free energy functional. From this viewpoint, the Expectation-Maximisation (EM) algorithm admits a natural interpretation as a coordinate descent method over the joint space of model parameters and probability measures. Recently, a significant body of work has adopted this perspective, leading to interacting particle algorithms for MMLE. In this paper, we propose an accelerated version of one such procedure, based on Stein variational gradient descent (SVGD), by introducing Nesterov acceleration in both the parameter updates and in the space of probability measures. The resulting method, termed Momentum SVGD-EM, consistently accelerates convergence in terms of required iterations across various tasks of increasing difficulty, demonstrating effectiveness in both low- and high-dimensional settings.
title	Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
topic	Machine Learning Computation
url	https://arxiv.org/abs/2603.08676

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