{
  "abstract": "We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking level. We develop a unified, model-based framework for policy learning from censored data, built on a new cost decomposition for base-stock policies and a biased sample-average approximation (SAA) approach. The cost decomposition allows us to propose a new coverage condition under which censored observations are informative enough for sample-efficient policy learning. Guided by this coverage condition, we design two biased SAA algorithms: an upper-biased one that achieves near-optimal sample complexity under the offline coverage condition, and a lower-biased one that actively generates the required coverage and achieves near-optimal regret online. More broadly, this biased SAA approach provides a general principle for implementing pessimism and optimism under censored feedback, which may be of independent interest.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.39397",
  "author_total": 4,
  "authors": [
    {
      "name": "Yuxuan Han"
    },
    {
      "name": "Xiaoyu Fan"
    },
    {
      "name": "Jiawei Zhang"
    },
    {
      "name": "Zhengyuan Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2xjv96e9tetbv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.39397",
  "fulltext_url": null,
  "id": "2xjv96e9tetbv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.39397",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1404|Supply Chain and Inventory Management"
    ]
  ],
  "openalex": "W7215044448",
  "pdf_url": "https://arxiv.org/pdf/2609.39397",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2xjv96e9tetbv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:58Z",
      "native_id": "2609.39397",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.39397"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach",
  "title_text": "Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:58Z",
  "url": "https://research.draconitic.com/paper/2xjv96e9tetbv",
  "venue": "arXiv",
  "versions": []
}