This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustrating that using a GP model with parameters selected by maximum likelihood often results in predictions that are not optimally calibrated. CP methods can adjust the prediction intervals, leading to better uncertainty quantification while maintaining the accuracy of the underlying GP model. We compare different CP variants and introduce a novel variant based on an asymmetric score. Our numerical experiments demonstrate the effectiveness of CP methods in improving calibration without compromising accuracy. This work aims to facilitate the adoption of CP methods in the GP community.

翻译：本文提倡在 Gaussian process (GP) 插值中采用保形预测 (conformal prediction, CP) 方法，以提升预测区间的校准性能。我们首先说明，使用通过极大似然法选择参数的 GP 模型进行预测，其结果往往未能达到最优校准。CP 方法可以调整预测区间，从而在保持底层 GP 模型准确性的同时，实现更优的不确定性量化。我们比较了不同的 CP 变体，并引入了一种基于非对称评分函数的新变体。数值实验表明，CP 方法能在不牺牲准确性的前提下有效改善校准效果。本研究旨在推动 CP 方法在 GP 领域中的应用。