DPPy: Sampling Determinantal Point Processes with Python

Gautier, Guillaume; Bardenet, Remi; Valko, Michal

Document type :

Article dans une revue scientifique: Article original

Title :

DPPy: Sampling Determinantal Point Processes with Python

Author(s) :

Gautier, Guillaume [Auteur]
Sequential Learning [SEQUEL]
Bardenet, Remi [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Valko, Michal [Auteur]

Sequential Learning [SEQUEL]

Journal title :

Journal of Machine Learning Research

Publisher :

Microtome Publishing

Publication date :

2019

ISSN :

1532-4435

English keyword(s) :

determinantal point processes
sampling schemes

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

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. ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Inférence bayésienne à ressources limitées - données massives et modèles coûteux

Collections :