English(EN) Co-optimization for Adaptive Conformal Prediction

新的CoCP方法改进了回归的自适应一致性预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员推出了一种新颖的回归任务自适应一致性预测方法CoCP。该方法通过同时参数化预测区间中心和半径，解决了异方差和偏态数据中固定区间中心导致的效率低下问题。CoCP通过对折叠残差进行平滑分位数回归来学习半径，并使用平滑区间损失来优化中心，从而纠正了中心偏差并将区间导向高密度区域。该方法通过分层一致性校准证明了其有限样本边际有效性，并在基础估计器一致的情况下实现了渐近条件覆盖和最优区间长度，在实验中显示出改进的经验条件覆盖可靠性和更窄的区间。 AI

影响提高了回归任务中预测区间的可靠性和效率，有可能增强AI模型在不确定环境中的准确性。

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在 arXiv stat.ML 阅读 →

conformal prediction

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Xiaoyi Su, Zhixin Zhou, Rui Luo · 2026-11-02 05:00

Co-optimization for Adaptive Conformal Prediction

arXiv:2603.01719v2 Announce Type: replace Abstract: In regression, conformal prediction often suffers from inefficiency under heteroscedasticity and skewness due to fixed, non-adaptive interval centering. We propose CoCP, a new method that parameterizes prediction intervals by a …

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Co-optimization for Adaptive Conformal Prediction

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