mlsanityData to 14 Oct 2026

Calibration Count Reuse: Validity Does Not Determine Efficiency

arXiv 2609.25138 ↗First posted 21 Sep 2026stat.ME cs.CV cs.LGcited by 0

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Abstract (arXiv)

Calibration count reuse raises separate validity and efficiency questions. We give a validity criterion for general count-dependent nonconformity scores: transferring one count from another class to the scored class must not improve its conformity. A leave-self-out full conformal reference proves the criterion without requiring normalization or preservation of same-class score order. For a common separable transformation, universal exchangeable validity is equivalent to being nondecreasing in the count, provided Kα ≥ 1; normalized multiplicative weights obey the complementary nonincreasing condition. Additive penalties are covered under the stated information restrictions. Efficiency has no parallel ordering: two iid constructions make the same valid rule improve or worsen expected size at unchanged coverage. An expanded 55-rule study finds no resolved advantage from selected live-count rules over uniform weights. Image studies identify undercoverage under iid resampling, including at numerical convergence. Separately, execution of the released Conf-OT pipeline on its DTD and Aircraft benchmark subsets produces near-nominal median coverage under fixed stratified counts. The native results are reported separately from the iid analyses, without treating a benchmark observation as a universal guarantee. The findings separate validity, classifier confidence, numerical convergence, and population-specific efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 21 Sep 2026.

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References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  4. 2013Fine-Grained Visual Classification of Aircraftcited by 1,726
  5. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  6. 2020Long-tail learning via logit adjustmentcited by 618
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  8. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
  9. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  10. 2023Transductive conformal inference with adaptive scorescited by 21
  11. 2021Stable Conformal Prediction Setscited by 19
  12. 2025Conformal Prediction for Zero-Shot Modelscited by 15
6 more
  1. 2024Conformal prediction after data-dependent model selectioncited by 14
  2. 2025Full Conformal Adaptation of Medical Vision-Language Modelscited by 9
  3. 2025Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Predictioncited by 8
  4. 2025Parametric Scaling Law of Tuning Bias in Conformal Predictioncited by 7
  5. 2025Conformal Prediction Meets Long-tail Classificationcited by 5
  6. 2025Conformal Inference for Open-Set and Imbalanced Classificationcited by 3

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