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Article Dans Une Revue Journal of Machine Learning Research Année : 2019

DPPy: Sampling Determinantal Point Processes with Python

Guillaume Gautier
Michal Valko

Résumé

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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Dates et versions

hal-01879424 , version 1 (23-09-2018)

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  • HAL Id : hal-01879424 , version 1

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Guillaume Gautier, Rémi Bardenet, Michal Valko. DPPy: Sampling Determinantal Point Processes with Python. Journal of Machine Learning Research, 2019. ⟨hal-01879424⟩
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