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JMLR 2019

DPPy: DPP Sampling with Python

Journal Article Articles Artificial Intelligence · Machine Learning

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 ] &copy JMLR 2019. ( edit, beta )

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Context

Venue
Journal of Machine Learning Research
Archive span
2000-2026
Indexed papers
4180
Paper id
935071466431257299