{
  "abstract": "Per-point data-valuation scores -- Data Shapley, influence, leverage -- are routinely used to select training data: keep the points with the top $k$ scores. We argue that top-$k$ by a fixed, mode-blind per-point score is the wrong selection rule precisely in the regime where data selection matters. The reason is that a point's value is not a fixed number but depends on what else has been selected -- a near-duplicate of an already-chosen point adds almost nothing, however high its score -- and a per-point scalar cannot express this interaction. The theory quantifies this failure exactly: in an equal latent-mode model, fixed top-$k$ selection incurs a closed-form excess risk over adaptive coverage because it repeatedly spends budget on already-covered modes. The gap is largest when the budget is tight, the learner representation is redundant, and missing a mode is costly. The practical rule is simple: use balance when the labels available at selection identify the learner's modes, and use explicit coverage when they do not. On standard classification tasks with strong pretrained features, class-balancing the same per-point scores recovers the missing coverage and matches or outperforms generic set coverage across our datasets. Without labels, label-free coverage improves accuracy by up to +0.20 while raw top-$k$ can fall below random; when labels are coarser than the modes, coverage within coarse strata performs best. We turn this distinction into a pre-specified diagnostic and validate it across both favorable and stress-test settings. The limits are part of the claim: the advantage is representation-relative. Coverage fails when the relevant modes are not expressed in the learner geometry, as with spurious subgroups, or when data quality varies independently of coverage -- exactly as the theory predicts.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Transactions on Machine Learning Research, 2026",
  "arxiv": "2610.34801",
  "author_total": 2,
  "authors": [
    {
      "name": "Yingjie Bai"
    },
    {
      "name": "Weiming Zhi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5dc8sdnsfe4at.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34801",
  "fulltext_url": null,
  "id": "5dc8sdnsfe4at",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34801",
  "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|Machine Learning and Data Classification"
    ]
  ],
  "openalex": "W7223793027",
  "pdf_url": "https://arxiv.org/pdf/2610.34801",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5dc8sdnsfe4at.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2610.34801",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34801"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation",
  "title_text": "When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/5dc8sdnsfe4at",
  "venue": "arXiv",
  "versions": []
}