Results 11 to 20 of about 424 (215)
Large-Margin Determinantal Point Processes [PDF]
CoRR, 2014 15 ...
Wei-Lun Chao +3 more
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On a few statistical applications of determinantal point processes
ESAIM: Proceedings and Surveys, 2017 Determinantal point processes (DPPs) are a repulsive distribution over configurations of points. The 2016 conference Journées Modélisation Aléatoire et Statistique (MAS) of the French society for applied and industrial mathematics (SMAI) featured a ...
Bardenet Rémi +3 more
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Large deviations of the interference in the Ginibre network model
Stochastic Systems, 2014 Under different assumptions on the distribution of the fading random variables, we derive large deviation estimates for the tail of the interference in a wireless network model whose nodes are placed, over a bounded region of the plane, according to the ...
Giovanni Luca Torrisi, Emilio Leonardi
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Kronecker Determinantal Point Processes
CoRR, 2016 Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks such as sampling and learning.
Mariet, Zelda Elaine, Sra, Suvrit
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On simulation of continuous determinantal point processes
Statistics and Computing, 2023 AbstractWe review how to simulate continuous determinantal point processes (DPPs) and improve the current simulation algorithms in several important special cases as well as detail how certain types of conditional simulation can be carried out. Importantly we show how to speed up the simulation of the widely used Fourier based projection DPPs, which ...
Frédéric Lavancier, Ege Rubak
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Learning Determinantal Point Processes
CoRR, 2012 Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among many remarkable properties, DPPs offer tractable algorithms for exact inference, including computing marginal probabilities and sampling; however, an important open question ...
Alex Kulesza, Ben Taskar
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Wasserstein Learning of Determinantal Point Processes
CoRR, 2020 Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on maximum likelihood estimation (MLE). While efficient and scalable, MLE approaches do not leverage any subset similarity information and may fail to recover the true ...
Lucas Anquetil +4 more
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, 2016 In this survey we review two topics concerning determinantal (or fermion) point processes. First, we provide the construction of diffusion processes on the space of configurations whose invariant measure is the law of a determinantal point process. Second, we present some algorithms to sample from the law of a determinantal point process on a finite ...
Laurent Decreusefond +3 more
+5 more sources
Spatio-Temporal Determinantal Point Processes
, 2023 9 pages, 1 ...
Vafaei, Nafiseh +3 more
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Learning Nonsymmetric Determinantal Point Processes
CoRR, 2019 Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPPs are conventionally parameterized by a positive semi-definite kernel matrix, and this symmetric kernel encodes only repulsive interactions between items.
Mike Gartrell +3 more
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