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