mlsanityData to 7 Oct 2026

Learning the Parameters of Determinantal Point Process Kernels

arXiv 1402.4862 ↗First posted 20 Feb 2014stat.ML cs.LGcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2014.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 3; 2024: 1; 2025: 2; 2026: 3.

21 papers.

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  1. 2021Modelling Behavioural Diversity for Learning in Open-Ended Gamescited by 39
  2. 2021Probabilistic Generating Circuitscited by 24
  3. 2022Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samplescited by 10
  4. 2022t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learningcited by 9
  5. 2022Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processescited by 7
  6. 2023Feasible Recourse Plan via Diverse Interpolationcited by 6
  7. 2021Gaussian Determinantal Processes: a new model for directionality in datacited by 4
  8. 2022Hardness of Maximum Likelihood Learning of DPPscited by 3
  9. 2026Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agentscited by 2
  10. 2021Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluatecited by 1
  11. 2023An Interpretable Determinantal Choice Model for Subset Selectioncited by 1
  12. 2021Determinantal consensus clusteringcited by 0
9 more
  1. 2021Nonparametric estimation of continuous DPPs with kernel methodscited by 0
  2. 2021Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processescited by 0
  3. 2022Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequencescited by 0
  4. 2023Efficient Failure Pattern Identification of Predictive Algorithmscited by 0
  5. 2024Duality induced by an embedding structure of determinantal point processcited by 0
  6. 2025A simple estimator of the correlation kernel matrix of a determinantal point processcited by 0
  7. 2025MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrievalcited by 0
  8. 2026Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendationcited by 0
  9. 2026FlexRouter: Learning Complementary Model Sets for Flexible LLM Routingcited by 0