DPPy: Sampling DPPs with Python

Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko
9/19/2018
cs.LGstat.ML

Official code: guilgautier/DPPy (240 stars, MIT, last commit 2024-08-14).

Abstract

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHub and equipped with an extensive documentation.

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Code Implementations(1)

guilgautier/DPPyOfficial100%
MIT

Python toolbox for sampling Determinantal Point Processes

24056Shell, PythonApr 13, 20182 years agoMIT
determinantal-point-processesdeterminantal-processespythonrandom-matrix-theorysampling-methods

Cite this paper

@article{gautier2018dppy,
  title  = {DPPy: Sampling DPPs with Python},
  author = {Guillaume Gautier and Guillermo Polito and Rémi Bardenet and Michal Valko},
  year   = {2018},
  eprint = {1809.07258},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.07258}
}

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