DPPy: Sampling Determinantal Point Processes with Python

Gautier, Guillaume; Bardenet, Remi; Valko, Michal

Type de document :

Compte-rendu et recension critique d'ouvrage

Titre :

DPPy: Sampling Determinantal Point Processes with Python

Auteur(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]

Titre de la revue :

Journal of Machine Learning Research

Éditeur :

Microtome Publishing

Date de publication :

2019

ISSN :

1532-4435

Mot(s)-clé(s) en anglais :

determinantal point processes
sampling schemes

Discipline(s) HAL :

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

Résumé en anglais : [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. ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

Inférence bayésienne à ressources limitées - données massives et modèles coûteux

Collections :