{
  "abstract": "Blockwise missingness in multimodal data is usually treated as an incomplete-input problem. We instead focus on prediction for a prespecified observed-modality pattern, where the observed modality set determines the information on which the prediction rule can condition. A procedure that imputes missing modalities, zero-fills unobserved modalities, or trains a single pooled predictor may borrow information across patterns, but it can also mix pattern-specific prediction rules. We propose Multimodal Overlap-aware Shared-specific Alignment and Inter-pattern Calibration (MOSAIC), a pattern-calibrated framework for borrowing across missingness patterns without collapsing their prediction rules. MOSAIC learns shared and modality-specific representations, uses the available representations that overlap with the target pattern to fit a first-stage predictor, and then estimates the calibration gap from target-pattern data. We establish non-asymptotic bounds that decompose the error into overlap effective sample size, calibration gap, and representation-learning error, clarifying when cross-pattern borrowing improves over local fitting and when the improvement is controlled by rule mismatch or representation-learning error. Simulations examine representation recovery and target-pattern correction, and applications to ICU mortality prediction, emotion recognition, and glaucoma classification show gains when target-pattern samples are limited or pattern-specific rules differ.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.01821",
  "author_total": 4,
  "authors": [
    {
      "name": "Junhan Yu"
    },
    {
      "name": "Kejian Zhang"
    },
    {
      "name": "Doudou Zhou"
    },
    {
      "name": "Guojun Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/65d4vw5rmza7b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.01821",
  "fulltext_url": null,
  "id": "65d4vw5rmza7b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.01821",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7167243478",
  "pdf_url": "https://arxiv.org/pdf/2607.01821",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/65d4vw5rmza7b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:26Z",
      "native_id": "2607.01821",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.01821"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Pattern-Calibrated Multimodal Prediction under Blockwise Missingness",
  "title_text": "Pattern-Calibrated Multimodal Prediction under Blockwise Missingness",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:26Z",
  "url": "https://research.draconitic.com/paper/65d4vw5rmza7b",
  "venue": "arXiv",
  "versions": []
}