{
  "abstract": "Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncertainty quantification (UQ), limiting their usefulness in high-stakes scenarios. We propose conformalized SNN (CF-SNN), a framework that, given a simplicial-complex entity with m prediction targets, generates prediction sets for all m targets simultaneously. Under mild assumptions, CF-SNN guarantees that, for each target, the prediction set contains the true label with a user-specified nominal coverage probability, and the realized miscoverage proportion across all m targets is guaranteed to remain close to the nominal level with high probability. Extensive experiments across a wide range of SC prediction tasks demonstrate that CF-SNN is a flexible UQ framework that achieves user-specified coverage while producing informative prediction sets of small size.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41166",
  "author_total": 4,
  "authors": [
    {
      "name": "Lekun Wang"
    },
    {
      "name": "Jaidev Goel"
    },
    {
      "name": "Yulia R. Gel"
    },
    {
      "name": "Yao Xie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ejva6reyz1s5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41166",
  "fulltext_url": null,
  "id": "1ejva6reyz1s5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41166",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41166",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1ejva6reyz1s5.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:25Z",
      "native_id": "2610.41166",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41166"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks",
  "title_text": "Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/1ejva6reyz1s5",
  "venue": "arXiv",
  "versions": []
}