{
  "abstract": "Many real-world experimental design problems require a batch of experimental runs across stages, in which multiple points are selected and evaluated at each stage. However, most work in the design literature is focused on fully sequential (point-by-point) methods. This paper proposes a sampling-based framework to systematically convert a fully sequential method to a batch sequential method. In particular, Stein variational gradient descent (SVGD) is adapted to efficiently sample a batch of points from a properly constructed target distribution while balancing the individual utility and the batch diversity. We address challenges that arise in using SVGD for experimental designs, including constrained design regions and near-uniform target distributions. We apply the proposed method to obtain batch versions of the state-of-the-art fully sequential methods, and demonstrate their performance through extensive numerical studies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.20583",
  "author_total": 5,
  "authors": [
    {
      "name": "Penghui Fu"
    },
    {
      "name": "Xiaoxian Ding"
    },
    {
      "name": "Chunlin Ji"
    },
    {
      "name": "Jianhua Z. Huang"
    },
    {
      "name": "C. F. Jeff Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dbjx4qqj629ax.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.20583",
  "fulltext_url": null,
  "id": "dbjx4qqj629ax",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.20583",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Optimal Experimental Design Methods"
    ]
  ],
  "openalex": "W7213576932",
  "pdf_url": "https://arxiv.org/pdf/2609.20583",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dbjx4qqj629ax.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:12Z",
      "native_id": "2609.20583",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.20583"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent",
  "title_text": "Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:12Z",
  "url": "https://research.draconitic.com/paper/dbjx4qqj629ax",
  "venue": "arXiv",
  "versions": []
}