{
  "abstract": "Determinantal point processes (DPPs) on a finite set are probability models that exhibit repulsion where inter-item similarities are encoded by a positive definite kernel matrix. Scaling DPP learning to large datasets typically requires imposing structural constraints on the kernel matrix, such as a low-rank-plus-diagonal form. However, structural assumptions introduce a severe trade-off between expressive power and computational scalability. Our study addresses this challenge by (a) modeling the full-rank interactions via a nonlinear kernel function and latent features, (b) designing a surrogate function for the maximum likelihood estimator, and (c) adopting stochastic optimization using random features of the nonlinear kernel. Numerical experiments on real-world datasets demonstrate that our method significantly improves this trade-off, achieving better generalization performance with remarkable parameter efficiency.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12829",
  "author_total": 2,
  "authors": [
    {
      "name": "Takahiro Kawashima"
    },
    {
      "name": "Hideitsu Hino"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6p60geez8z72k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12829",
  "fulltext_url": null,
  "id": "6p60geez8z72k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12829",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7221516153",
  "pdf_url": "https://arxiv.org/pdf/2610.12829",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6p60geez8z72k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.12829",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12829"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions",
  "title_text": "Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/6p60geez8z72k",
  "venue": "arXiv",
  "versions": []
}