@article{gautier2019dppy,
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. 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.},
author = {Gautier, Guillaume and Bardenet, R{\'{e}}mi and Valko, Michal},
journal = {Journal of Machine Learning Research},
title = {{DPPy: Sampling determinantal point processes with Python}},
year = {2019}
}