Universal distribution of the empirical coverage in split conformal prediction

[2303.02770] Universal distribution of the empirical coverage in split conformal prediction Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Statistics Theory arXiv:2303.02770 (math) [Submitted on 5 Mar 2023 ( v1 ), last revised 21 Sep 2024 (this version, v2)] Title: Universal distribution of the empirical coverage in split conformal prediction Authors: Paulo C. Marques F View a PDF of the paper titled Universal distribution of the empirical coverage in split conformal prediction, by Paulo C. Marques F View PDF HTML (experimental) Abstract: When split conformal prediction operates in batch mode with exchangeable data, we determine the exact distribution of the empirical coverage of prediction sets produced for a finite batch of future observables, as well as the exact distribution of its almost sure limit when the batch size goes to infinity. Both distributions are universal, being determined solely by the nominal miscoverage level and the calibration sample size, thereby establishing a criterion for choosing the minimum required calibration sample size in applications. Comments: 6 pages, 1 table Subjects: Statistics Theory (math.ST) ; Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2303.02770 [math.ST] (or arXiv:2303.02770v2 [math.ST] for this version) https://doi.org/10.48550/arXiv.2303.02770 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Statistics & Probability Letters 219, 110350 (2025) Related DOI : https://doi.org/10.1016/j.spl.2024.110350 Focus to learn more DOI(s) linking to related resources Submission history From: Paulo C. Marques F. [ view email ] [v1] Sun, 5 Mar 2023 20:46:01 UTC (435 KB) [v2] Sat, 21 Sep 2024 23:17:08 UTC (9 KB) Full-text links: Access Paper: View a PDF of the paper titled Universal distribution of the empirical coverage in split conformal prediction, by Paulo C. Marques F View PDF HTML (experimental) TeX Source view license Current browse context: math.ST < prev | next > new | recent | 2023-03 Change to browse by: cs cs.LG math stat stat.ML stat.TH References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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