Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
Abstract
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.
Cite
@article{arxiv.2509.22529,
title = {Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability},
author = {Mingyi Zheng and Hongyu Jiang and Yizhou Lu and Jiaye Teng},
journal= {arXiv preprint arXiv:2509.22529},
year = {2025}
}