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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.22529 |
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| _version_ | 1866911178688561152 |
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| author | Zheng, Mingyi Jiang, Hongyu Lu, Yizhou Teng, Jiaye |
| author_facet | Zheng, Mingyi Jiang, Hongyu Lu, Yizhou Teng, Jiaye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability Zheng, Mingyi Jiang, Hongyu Lu, Yizhou Teng, Jiaye Machine Learning 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. |
| title | Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.22529 |