# Duality induced by an embedding structure of determinantal point process

arXiv: https://arxiv.org/abs/2404.11024 · HTML: https://mlsanity.com/paper/2404.11024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Learning the Parameters of Determinantal Point Process Kernels](https://mlsanity.com/paper/1402.4862/) (2014)
- [Monte Carlo with Determinantal Point Processes](https://mlsanity.com/paper/1605.00361/) (2016)
- [Expectation-Maximization for Learning Determinantal Point Processes](https://mlsanity.com/paper/1411.1088/) (2014)
- [Learning Determinantal Point Processes in Sublinear Time](https://mlsanity.com/paper/1610.05925/) (2016)
- [Kronecker Determinantal Point Processes](https://mlsanity.com/paper/1605.08374/) (2016)
- [Hardness of Maximum Likelihood Learning of DPPs](https://mlsanity.com/paper/2205.12377/) (2022)
- [Diversifying Sparsity Using Variational Determinantal Point Processes](https://mlsanity.com/paper/1411.6307/) (2014)
- [Minimum information dependence modeling](https://mlsanity.com/paper/2206.06792/) (2022)

## Abstract (arXiv)

This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family is analyzed using the e-embedding curvature tensor, which identifies partially flat parameters of a DPP. On the basis of this embedding structure, the duality related to a marginal kernel and an L-ensemble kernel is discovered.