# A simple estimator of the correlation kernel matrix of a determinantal point process

arXiv: https://arxiv.org/abs/2505.14529 · HTML: https://mlsanity.com/paper/2505.14529/

## 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.

## Builds on (most cited first, 7 of 7)

- [Learning the Parameters of Determinantal Point Process Kernels](https://mlsanity.com/paper/1402.4862/) (2014)
- [Learning Nonsymmetric Determinantal Point Processes](https://mlsanity.com/paper/1905.12962/) (2019)
- [Low-Rank Factorization of Determinantal Point Processes for Recommendation](https://mlsanity.com/paper/1602.05436/) (2016)
- [Exact Sampling of Determinantal Point Processes without Eigendecomposition](https://mlsanity.com/paper/1802.08429/) (2018)
- [Hardness of Maximum Likelihood Learning of DPPs](https://mlsanity.com/paper/2205.12377/) (2022)
- [Adaptive estimating function inference for non-stationary determinantal point processes](https://mlsanity.com/paper/1806.06231/) (2018)
- [Modeling Hypergraphs with Diversity and Heterogeneous Popularity](https://mlsanity.com/paper/2501.04251/) (2025)

## 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.