arXiv Machine Learning October 3, 2026 By Hansheng Jiang

Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures

Read the original on arXiv Machine Learning →

arXiv:2610. 01088v1 Announce Type: cross Abstract: The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

efficiency

arXiv Statistics ML

Sep 16

Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transport

arXiv:2609.16622v1 Announce Type: cross Abstract: Parameter estimation in finite mixture models can exhibit highly heterogeneous convergence behavior: locally isolated components may be estimated sub...

By Dung Le, Huy Nguyen, Trang Pham, Alessandro Rinaldo, Nhat Ho

arXiv Machine Learning

Jul 17

Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs

arXiv:2607. 14304v1 Announce Type: cross Abstract: We study sparse random geometric graphs generated by connecting pairs of high-dimensional vectors whose inner product exceeds a threshold.

By Manuel Fernandez V, Yizhe Zhu

arXiv Machine Learning

Jun 11

Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more

arXiv:2606. 11469v1 Announce Type: cross Abstract: We study the task of density estimation, where we hope to accurately estimate a probability density from $n$ samples.

By Spencer Compton, Jerry Li

arXiv Machine Learning

Sep 10

The EM-algorithm and the Method of Moments in Softmax Mixture Models

arXiv:2409. 09903v3 Announce Type: replace-cross Abstract: Softmax Mixture Models (SMMs) are discrete $K$-component mixture models for the probabilities of selecting one of $p$ candidate feature vectors $X_1,\ldots,X_p\in\mathbb{R}^L$ in heterogeneous populations and are widely used in econometrics and scientific applications.

By Xin Bing, Florentina Bunea, Jonathan Niles-Weed, Marten Wegkamp

llms

arXiv Machine Learning

Sep 23

Statistical Rates for Entropic Optimal Transport in the Discrete to SubGaussian Regime

arXiv:2609. 26647v1 Announce Type: cross Abstract: We study statistical rates in entropic optimal transport in the semi-discrete regime where one measure has finite support and the other is subGaussian.

By Tomas Gonzalez, Gonzalo Mena

arXiv Machine Learning

Jun 24

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

arXiv:2509. 22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions.

By Sre\'cko {\DJ}ura\v{s}inovi\'c, Jean-Bernard Lasserre, Victor Magron

One email a morning, machine-written

Related stories

Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transport

Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs

Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more

The EM-algorithm and the Method of Moments in Softmax Mixture Models

Statistical Rates for Entropic Optimal Transport in the Discrete to SubGaussian Regime

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach