{
  "abstract": "Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcoverage even when point predictions are accurate. We introduce C-USIM (Conditionally-Uniformized Score Integration Method), a lightweight application of highest predictive density split conformal prediction that accommodates multimodal predictions. Given calibration and test outputs, it requires no additional training or model inference. It provides finite-sample marginal validity under our assumptions. We bound conditional-marginal coverage gaps using distribution-estimation error and score discreteness, and examine coverage heterogeneity through percentile rank-score plots. Experiments with TabPFN and TabICL show improved marginal coverage accuracy and lower average conditional and group coverage errors. Under a fixed data budget, allocating more observations to calibration can reduce marginal coverage error despite less accurate point predictions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34887",
  "author_total": 3,
  "authors": [
    {
      "name": "Sungwoo Park"
    },
    {
      "name": "Sunghee Park"
    },
    {
      "name": "Won Chang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3126jsxy7hxp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34887",
  "fulltext_url": null,
  "id": "f3126jsxy7hxp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34887",
  "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": "W7214726182",
  "pdf_url": "https://arxiv.org/pdf/2609.34887",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f3126jsxy7hxp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:26Z",
      "native_id": "2609.34887",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34887"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Conformal Prediction and Conditional Coverage for Tabular Foundation Models",
  "title_text": "Conformal Prediction and Conditional Coverage for Tabular Foundation Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:26Z",
  "url": "https://research.draconitic.com/paper/f3126jsxy7hxp",
  "venue": "arXiv",
  "versions": []
}