{
  "abstract": "Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, quantile risk control extends this framework to quantile-based risk measures. However, existing methods either suffer from excessive conservatism or lack rigorous finite-sample guarantees. To address these limitations, we introduce Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity. Our key idea is to formulate risk control as a finite-occupancy problem by partitioning the loss space with the ordered calibration losses. Specifically, we estimate the distribution of test losses across the resulting bins and upper-bound the risk by the maximum loss attained within each bin. We then select the parameter $λ$ such that this upper bound does not exceed a predefined threshold $α$ with high probability $1-δ$. Theoretically, we establish a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\\mathcal{O}_ p(n^{-1/2})$. Extensive experiments demonstrate the effectiveness of our method, reducing the risk gap by up to 78.64\\% on common benchmarks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03104",
  "author_total": 4,
  "authors": [
    {
      "name": "Zihao Shi"
    },
    {
      "name": "Huajun Xi"
    },
    {
      "name": "Bingyi Jing"
    },
    {
      "name": "Hongxin Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5r98znsw8hnzy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03104",
  "fulltext_url": null,
  "id": "5r98znsw8hnzy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03104",
  "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",
      "1702|Adversarial Robustness in Machine Learning"
    ]
  ],
  "openalex": "W7208762594",
  "pdf_url": "https://arxiv.org/pdf/2609.03104",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5r98znsw8hnzy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:42Z",
      "native_id": "2609.03104",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03104"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Occupancy-based Quantile Risk Control",
  "title_text": "Occupancy-based Quantile Risk Control",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:42Z",
  "url": "https://research.draconitic.com/paper/5r98znsw8hnzy",
  "venue": "arXiv",
  "versions": []
}