Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability

• URL: http://arxiv.org/abs/2509.22529v1
• Date: Fri, 26 Sep 2025 16:08:26 GMT
• Title: Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
• Authors: Mingyi Zheng, Hongyu Jiang, Yizhou Lu, Jiaye Teng,
• Abstract summary: Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets.<n>We propose SCD-split, which incorporates smoothing operations into the CP framework.
• Score: 8.222748526991863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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