{
  "abstract": "We study distribution testing with access to non-adaptive conditional samples. Specifically, we give tight bounds for equivalence testing, determining whether two unknown distributions are equal to or $\\varepsilon$-far from each other in total variation distance. Our algorithm and lower bound show that $\\tilde Θ\\left(\\frac{\\log n}{\\varepsilon^2}\\right)$ queries are necessary and sufficient for this problem. These results demonstrate that the complexity of uniformity, identity, and equivalence testing with non-adaptive conditional samples are all $\\tilde Θ(\\log n)$.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11145",
  "author_total": 1,
  "authors": [
    {
      "name": "Gautam Kamath"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/13bw9ccfn74jp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11145",
  "fulltext_url": null,
  "id": "13bw9ccfn74jp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11145",
  "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.DS"
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      "cs.IT"
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      "arxiv",
      "math.IT"
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      "arxiv",
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  "openalex": "W7221175102",
  "pdf_url": "https://arxiv.org/pdf/2610.11145",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/13bw9ccfn74jp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:58Z",
      "native_id": "2610.11145",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11145"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Tight Bounds for Equivalence Testing with Non-Adaptive Conditional Samples",
  "title_text": "Tight Bounds for Equivalence Testing with Non-Adaptive Conditional Samples",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/13bw9ccfn74jp",
  "venue": "arXiv",
  "versions": []
}