Home Page

Papers

Submissions

News

Editorial Board

Open Source Software

Proceedings (PMLR)

Transactions (TMLR)

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

DPPy: DPP Sampling with Python

Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko; 20(180):1−7, 2019.

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 for both finite and continuous DPPs. The project is hosted on GitHub, and equipped with an extensive documentation.

[abs][pdf][bib]        [code]
© JMLR 2019. (edit, beta)