Aggregating conformal prediction sets via 𝜶-allocation

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ID: 321761
2026
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Abstract
Abstract Conformal prediction offers a distribution-free framework for constructing prediction sets with finite-sample coverage. Yet, efficiently leveraging multiple nonconformity scores to reduce set sizes remains an open challenge. Instead of selecting a single best score, this work introduces a principled aggregation strategy that intersects multiple conformal prediction sets whose confidence levels are optimally allocated to minimize empirical set size while maintaining asymptotic coverage. Two variants are developed to guarantee finite-sample coverage via sample splitting and full conformalization, respectively. An individualized allocation strategy is further proposed to promote local size efficiency while achieving asymptotic conditional coverage. Extensive experiments on synthetic and real-world datasets demonstrate that our approach achieves considerably smaller prediction sets than state-of-the-art baselines while maintaining valid coverage.
Reference Key
openalex_W7169841369 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Congbin Xu, Yue Yu, Zhaojun Wang, Changliang Zou, Haojie Ren
Journal jurnal biometrika dan kependudukan
Year 2026
DOI
10.1093/biomet/asag048
URL
https://doi.org/10.1093/biomet/asag048
Keywords Keywords not found

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