Valid Selection among Conformal Sets

2025/06/25 by Mahmoud Hegazy, Liviu Aolaritei, Hegazy, Mahmoud +5 · 1 citation
Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fuzzy Systems and Optimization #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Other Statistics (stat.OT)

paper · pdf · doi:10.48550/arxiv.2506.20173

openalex publication_date 2025/06/25 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To address this challenge, we propose a stability-based approach that ensures coverage for the selected prediction set. We extend our results to the online conformal setting, propose several refinements in settings where additional structure is available, and demonstrate its effectiveness through experiments.

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