arXiv Machine Learning August 19, 2026 By Mo Zhou, Weihang Xu, Maryam Fazel, Simon S. Du

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

Read the original on arXiv Machine Learning →

arXiv:2506. 06584v2 Announce Type: replace Abstract: Learning Gaussian Mixture Models (GMMs) is a fundamental problem in statistics and machine learning, with the Expectation-Maximization (EM) algorithm and its popular variant gradient EM being arguably the most widely used algorithms in practice.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence

Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

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