DPPy: DPP Sampling with Python

Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko

JMLR 2019 pp. 1-7

/jmlr/2019/gautier2019jmlr-dppy/

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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Cite

Text

Gautier et al. "DPPy: DPP Sampling with Python." Journal of Machine Learning Research, 2019.

Markdown

[Gautier et al. "DPPy: DPP Sampling with Python." Journal of Machine Learning Research, 2019.](https://mlanthology.org/jmlr/2019/gautier2019jmlr-dppy/)

BibTeX

@article{gautier2019jmlr-dppy,
  title     = {{DPPy: DPP Sampling with Python}},
  author    = {Gautier, Guillaume and Polito, Guillermo and Bardenet, Rémi and Valko, Michal},
  journal   = {Journal of Machine Learning Research},
  year      = {2019},
  pages     = {1-7},
  volume    = {20},
  url       = {https://mlanthology.org/jmlr/2019/gautier2019jmlr-dppy/}
}