提出可拆分的混合密度建模框架，支持不同分布类型组合。
PMODE: Theoretically Grounded and Modular Mixture Modeling
- 分区域建模：将数据分区后分别拟合密度估计器
- 在高维场景下表现接近深度模型，CIFAR-10异常检测准确率领先
- 框架灵活，适用于参数与非参数组件混合建模
我们提出PMODE（分块密度估计混合模型），一种兼具参数与非参数组件的通用模块化混合建模框架。通过将数据划分并为每个子集独立拟合估计器构建混合模型，该方法在该类估计器中达到近似最优收敛速率，且在各组分来自不同分布族时仍保持有效性。作为应用，我们开发了MV-PMODE，将此前理论性方法扩展至数千维的高维密度估计场景。尽管结构简单，其在CIFAR-10异常检测任务上表现媲美深度基线模型。
原文摘要 · Abstract (English)
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.
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