{
  "abstract": "We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design's mean performance exceeds a target. Unlike best-arm identification, this objective does not require finding the best design. For Gaussian bandits with a Gaussian prior, we propose \\emph{Halve-Then-Greedy} (HTG), which combines a large initial balanced allocation, successive halving, and posterior-greedy sampling. We prove instance-dependent, finite-budget exponential bounds on posterior-failure probability that capture both separation from unfavorable arms and the presence of multiple favorable arms. On one-positive-arm families, we establish an asymptotic sample-complexity lower bound that HTG matches up to a logarithmic factor. We further analyze uniform sampling and sequential halving, revealing regimes where they require more samples than HTG by linear and logarithmic factors, respectively. Simulations illustrate HTG's sample-efficiency advantages and their dependence on instance structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20997",
  "author_total": 7,
  "authors": [
    {
      "name": "Yinan Li"
    },
    {
      "name": "Kwang-Sung Jun"
    },
    {
      "name": "Kapilan Balagopalan"
    },
    {
      "name": "Tuan Nguyen"
    },
    {
      "name": "Yao Zhao"
    },
    {
      "name": "Abbas Zaidi"
    },
    {
      "name": "Houssam Nassif"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1v9es4b7kfmzr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20997",
  "fulltext_url": null,
  "id": "1v9es4b7kfmzr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20997",
  "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",
      "1803|Advanced Bandit Algorithms Research"
    ]
  ],
  "openalex": "W7222608984",
  "pdf_url": "https://arxiv.org/pdf/2610.20997",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1v9es4b7kfmzr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.20997",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20997"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence",
  "title_text": "Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/1v9es4b7kfmzr",
  "venue": "arXiv",
  "versions": []
}