DPPy: Sampling Determinantal Point Processes with Python

Guillaume Gautier 1 Rémi Bardenet 2 Michal Valko 1
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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. The project is hosted on GitHub and equipped with an extensive documentation. This documentation takes the form of a short survey of DPPs and relates each mathematical property with DPPy objects.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-01879424
Contributor : Michal Valko <>
Submitted on : Sunday, September 23, 2018 - 9:32:15 PM
Last modification on : Friday, April 19, 2019 - 4:55:13 PM
Long-term archiving on : Monday, December 24, 2018 - 1:21:32 PM

File

gautier2018dppy.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01879424, version 1

Citation

Guillaume Gautier, Rémi Bardenet, Michal Valko. DPPy: Sampling Determinantal Point Processes with Python. 2018. ⟨hal-01879424⟩

Share

Metrics

Record views

120

Files downloads

166