2019
JMLR
JMLR 2019
DPPy: DPP Sampling with Python
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)
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— exact sampling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Hot Topic Early Bird
— probability distribution