# Learning the Parameters of Determinantal Point Process Kernels

arXiv: https://arxiv.org/abs/1402.4862 · HTML: https://mlsanity.com/paper/1402.4862/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2014.

## Built on by (most cited first, 23 of 23)

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- [Feasible Recourse Plan via Diverse Interpolation](https://mlsanity.com/paper/2302.11213/) (2023)
- [Gaussian Determinantal Processes: a new model for directionality in data](https://mlsanity.com/paper/2111.09990/) (2021)
- [Hardness of Maximum Likelihood Learning of DPPs](https://mlsanity.com/paper/2205.12377/) (2022)
- [MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval](https://mlsanity.com/paper/2507.06654/) (2025)
- [Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents](https://mlsanity.com/paper/2609.05824/) (2026)
- [Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluate](https://mlsanity.com/paper/2102.12646/) (2021)
- [An Interpretable Determinantal Choice Model for Subset Selection](https://mlsanity.com/paper/2302.11477/) (2023)
- [FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing](https://mlsanity.com/paper/2609.38585/) (2026)
- [Determinantal consensus clustering](https://mlsanity.com/paper/2102.03948/) (2021)
- [Nonparametric estimation of continuous DPPs with kernel methods](https://mlsanity.com/paper/2106.14210/) (2021)
- [Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes](https://mlsanity.com/paper/2111.14148/) (2021)
- [Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences](https://mlsanity.com/paper/2204.03571/) (2022)
- [Efficient Failure Pattern Identification of Predictive Algorithms](https://mlsanity.com/paper/2306.00760/) (2023)
- [Duality induced by an embedding structure of determinantal point process](https://mlsanity.com/paper/2404.11024/) (2024)
- [A simple estimator of the correlation kernel matrix of a determinantal point process](https://mlsanity.com/paper/2505.14529/) (2025)
- [Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation](https://mlsanity.com/paper/2602.02024/) (2026)
- [Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions](https://mlsanity.com/paper/2610.12829/) (2026)
- [CopRoute: Dependence-Aware Conformal Routing for Language Models](https://mlsanity.com/paper/2610.13276/) (2026)

## Abstract (arXiv)

Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature of the likelihood function. In this paper, we propose using Bayesian methods to learn the DPP kernel parameters. These methods are applicable in large-scale and continuous DPP settings even when the exact form of the eigendecomposition is unknown. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on spatial distribution of nerve fibers, and in studying human perception of diversity in images.