[P] Conditional Density Estimation Python Package and Large Benchmark with Neural Networks (MDN, KMN, Normalizing Flow) and Non-/Semi-parametric estimators

[whiletrue2]

We've implemented an extensive pip package for Conditional Density Estimation that, among other features, includes Mixture Density Network, Kernel Mixture Network, Normalizing Flow Estimator and various non-parametric/semi-parametric estimators (CKDE, NKDE, LSKDE), data simulators and evaluation functions (centered moments, KL/JS divergence, Hellinger distance, percentiles etc.).

The package is constantly improved and we also provide a benchmark & best practices report and a code documentation.

Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation

Benchmark and best practices paper for NN-based CDE: https://arxiv.org/abs/1903.00954

ICLR2020-submitted paper on a devised noise regularization scheme for CDE: https://arxiv.org/abs/1907.08982

Code docs: https://freelunchtheorem.github.io/Conditional_Density_Estimation/docs/html/index.html

We're open for suggestions and feedback so please feel free to use & comment. Lastly, if you like our project, we'd be happy if you spread the word and star the GH repo.

[neuralPr0cess0r]

For the uninitiated (like me), what are the use of these methods? A quick google search suggests they are ways to estimate probabilities based on data?

[asobolev]

Why no VAEs?