A simple estimator of the correlation kernel matrix of a determinantal point process
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Abstract (arXiv)
The Determinantal Point Process (DPP) is a parameterized model for multivariate binary variables, characterized by a correlation kernel matrix. This paper proposes a closed form estimator of this kernel, which is particularly easy to implement and can also be used as a starting value of learning algorithms for maximum likelihood estimation. We prove the consistency and asymptotic normality of our estimator, as well as its large deviation properties.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 20 May 2025.
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References · 7
- 2014Learning the Parameters of Determinantal Point Process Kernelscited by 23
- 2019Learning Nonsymmetric Determinantal Point Processescited by 16
- 2016Low-Rank Factorization of Determinantal Point Processes for Recommendationcited by 14
- 2018Exact Sampling of Determinantal Point Processes without Eigendecompositioncited by 8
- 2022Hardness of Maximum Likelihood Learning of DPPscited by 4
- 2018Adaptive estimating function inference for non-stationary determinantal point processescited by 3
- 2025Modeling Hypergraphs with Diversity and Heterogeneous Popularitycited by 2
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