PMODE: Theoretically Grounded and Modular Mixture Modeling
Machine Learning
2025-09-01 v1
Abstract
We introduce PMODE (Partitioned Mixture Of Density Estimators), a general and modular framework for mixture modeling with both parametric and nonparametric components. PMODE builds mixtures by partitioning the data and fitting separate estimators to each subset. It attains near-optimal rates for this estimator class and remains valid even when the mixture components come from different distribution families. As an application, we develop MV-PMODE, which scales a previously theoretical approach to high-dimensional density estimation to settings with thousands of dimensions. Despite its simplicity, it performs competitively against deep baselines on CIFAR-10 anomaly detection.
Cite
@article{arxiv.2508.21396,
title = {PMODE: Theoretically Grounded and Modular Mixture Modeling},
author = {Robert A. Vandermeulen},
journal= {arXiv preprint arXiv:2508.21396},
year = {2025}
}