标题： 标签噪声下的回归模型高效共形预测 显示英文标题

标题： Efficient Conformal Prediction for Regression Models under Label Noise

摘要： 在高风险场景中，如医学影像应用，为回归模型的预测配备可靠的置信区间至关重要。最近，合规预测（CP）作为一种强大的统计框架脱颖而出，它基于一个带标签的校准集，生成以预设概率包含真实标签的区间。在本文中，我们解决了当校准集包含噪声标签时，将CP应用于回归模型的问题。我们首先建立了一个数学基础明确的程序来估计无噪声的CP阈值。然后，我们将它转化为一种克服回归问题连续性带来的挑战的实际算法。我们在两个带有高斯标签噪声的医学影像回归数据集上评估了所提出的方法。我们的方法显著优于现有替代方案，性能接近于无噪声标签的情况。 显示英文摘要

摘要： In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals. Recently, Conformal Prediction (CP) has emerged as a powerful statistical framework that, based on a labeled calibration set, generates intervals that include the true labels with a pre-specified probability. In this paper, we address the problem of applying CP for regression models when the calibration set contains noisy labels. We begin by establishing a mathematically grounded procedure for estimating the noise-free CP threshold. Then, we turn it into a practical algorithm that overcomes the challenges arising from the continuous nature of the regression problem. We evaluate the proposed method on two medical imaging regression datasets with Gaussian label noise. Our method significantly outperforms the existing alternative, achieving performance close to the clean-label setting.