{
  "abstract": "We propose a new regression method named Direct Inference on Admissible Sets (Diona) that directly uses samples to estimate the state and its derivatives at a query point, along with their admissible bounds, without long global training. By casting the values and derivatives at the query and neighbouring samples as latent variables in a constrained optimisation problem, the burden of long training loops becomes one of on-demand local solves. We establish Diona's theoretical properties including finite-sample coverage and, in the noiseless case, convergence to the true state and derivatives under dense sampling. In a nonlinear regression benchmark on six functions of three to eight dimensions, Diona was more accurate than a Gaussian process (GP) on noiseless data especially with denser data sets. Furthermore, as with GP, it produced uncertainty bounds that contained the true value at more than 95% of query points in four functions. Diona's time to a first prediction after a data set update was three to four orders of magnitude shorter than that of the GP, and two to three orders shorter than that of a neural network, which was more accurate but offers no uncertainty quantification. This low latency is paid for by on-demand predictions, so this latency advantage applies when relatively few predictions are needed between data updates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.31526",
  "author_total": 2,
  "authors": [
    {
      "name": "Lorenzo Sabug Jr."
    },
    {
      "name": "Eric Kerrigan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4mdbg649ad08t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.31526",
  "fulltext_url": null,
  "id": "4mdbg649ad08t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.31526",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
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  "metadata_licence": "CC0-1.0",
  "native_subjects": [
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  "openalex": "W7223296561",
  "pdf_url": "https://arxiv.org/pdf/2610.31526",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4mdbg649ad08t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:38Z",
      "native_id": "2610.31526",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.31526"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Direct Inference on Admissible Sets (Diona)",
  "title_text": "Direct Inference on Admissible Sets (Diona)",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:38Z",
  "url": "https://research.draconitic.com/paper/4mdbg649ad08t",
  "venue": "arXiv",
  "versions": []
}