DPPy: Sampling Determinantal Point Processes with Python. 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.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Coeurjolly, Jean-François; Mazoyer, Adrien; Amblard, Pierre-Olivier: Monte Carlo integration of non-differentiable functions on ([0,1]^\iota), (\iota=1,\ldots, d), using a single determinantal point pattern defined on ([0,1]^d) (2021)
- Gautier, Guillaume; Bardenet, Rémi; Valko, Michal: Fast sampling from (\beta)-ensembles (2021)
- Bardenet, Rémi; Hardy, Adrien: Monte Carlo with determinantal point processes (2020)
- Belhadji, Ayoub; Bardenet, Rémi; Chainais, Pierre: A determinantal point process for column subset selection (2020)
- Burt, David R.; Rasmussen, Carl Edward; van der Wilk, Mark: Convergence of sparse variational inference in Gaussian processes regression (2020)
- Kammoun, Mohamed Slim: On the longest common subsequence of conjugation invariant random permutations (2020)
- Launay, Claire; Galerne, Bruno; Desolneux, Agnès: Exact sampling of determinantal point processes without eigendecomposition (2020)
- Guillaume Gautier, Rémi Bardenet, Michal Valko: DPPy: Sampling Determinantal Point Processes with Python (2018) arXiv