PMODE: Theoretically Grounded and Modular Mixture Modeling
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 75 references that are in the map. First posted on arXiv 29 Aug 2025.
Builds on
32 of 75 references.
References · 32
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- 2022Identifiability of deep generative models without auxiliary informationcited by 70
- 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
20 more
- 2017Deep Gaussian Mixture Modelscited by 30
- 2021DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detectioncited by 21
- 2013The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixturescited by 18
- 2018Identifiability of Nonparametric Mixture Models and Bayes Optimal Clusteringcited by 15
- 2020Low-rank Characteristic Tensor Density Estimation Part I: Foundationscited by 15
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- 2022An Equal-Size Hard EM Algorithm for Diverse Dialogue Generationcited by 11
- 2017Sample-Efficient Learning of Mixturescited by 9
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- 2021Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimationcited by 8
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- 2021Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Modelscited by 6
- 2021Uniform Consistency in Nonparametric Mixture Modelscited by 5
- 2024Dimension-independent rates for structured neural density estimationcited by 5
- 2023Sample Complexity Using Infinite Multiview Modelscited by 3
- 2024Breaking the curse of dimensionality in structured density estimationcited by 3
- 2017On w-mixtures: Finite convex combinations of prescribed component distributionscited by 2
- 2019Estimation of the Number of Components of Non-Parametric Multivariate Finite Mixture Modelscited by 2
- 2022Generalized Identifiability Bounds for Mixture Models with Grouped Samplescited by 2
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Citing papers per year: 2026: 1.
1 paper.