mlsanityData to 14 Oct 2026

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability

arXiv 2509.22529 ↗First posted 26 Sep 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets composed of multiple disconnected subintervals, which are difficult to interpret. In this paper, we propose SCD-split, which incorporates smoothing operations into the CP framework. Such smoothing operations potentially help merge the subintervals, thus leading to interpretable prediction sets. Experimental results on both synthetic and real-world datasets demonstrate that SCD-split balances the interval length and the number of disconnected subintervals. Theoretically, under specific conditions, SCD-split provably reduces the number of disconnected subintervals while maintaining comparable coverage guarantees and interval length compared with CD-split.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 73 references that are in the map. First posted on arXiv 26 Sep 2025.

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References · 45

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33 more
  1. 2023Conformal Prediction With Conditional Guaranteescited by 152
  2. 2022Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practicecited by 136
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