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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 - 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.
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Submitted on : Sunday, September 23, 2018 - 9:32:15 PM
Last modification on : Tuesday, January 4, 2022 - 6:14:39 AM
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  • HAL Id : hal-01879424, version 1


Guillaume Gautier, Rémi Bardenet, Michal Valko. DPPy: Sampling Determinantal Point Processes with Python. Journal of Machine Learning Research -- Machine Learning Open Source Software (JMLR MLOSS), 2019. ⟨hal-01879424⟩



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