Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability

Mingyi Zheng; Hongyu Jiang; Yizhou Lu; Jiaye Teng

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arXiv · 2509.22529

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability

Mingyi Zheng·Hongyu Jiang·Yizhou Lu·Jiaye Teng

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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.

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BibTeX RIS

Mingyi Zheng, Hongyu Jiang, Yizhou Lu, Jiaye Teng. 2025-09-26. Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability. https://arxiv.org/abs/2509.22529

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