{
  "abstract": "Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation. They successfully model complex random objects arising in real-world problems. In this work, we propose a sample-efficient training mechanism for these models. Our approach leverages regularized inference with shrinkage priors and generalized Bayes. In the large $p$ (parameter dimension) and small $n$ (sample size) setting, sparsity-inducing priors have been successfully used for linear regression when $p \\gg n$. However, their application to implicit generative models remains challenging due to the intractability of the likelihood function. To address this, we adapt a scoring rule posterior based on a strictly proper scoring rule, following the generalized Bayes framework. We then propose a block SGMCMC within Gibbs sampling mechanism. This enables efficient exploration of high-dimensional parameter spaces by identifying a sparse representation of neural implicit generative models. We demonstrate the performance of our proposed method through experiments on $p \\gg n$ linear regression and an application of neural generative models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05241",
  "author_total": 3,
  "authors": [
    {
      "name": "Shreya Sinha-Roy"
    },
    {
      "name": "Christian P. Robert"
    },
    {
      "name": "Ritabrata Dutta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8eqbc4a2j0pc6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05241",
  "fulltext_url": null,
  "id": "8eqbc4a2j0pc6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05241",
  "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.CO"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05241",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8eqbc4a2j0pc6.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:35Z",
      "native_id": "2611.05241",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05241"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Shrinkage prior for implicit generative models",
  "title_text": "Shrinkage prior for implicit generative models",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:35Z",
  "url": "https://research.draconitic.com/paper/8eqbc4a2j0pc6",
  "venue": "arXiv",
  "versions": []
}