Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions. These uncertainty estimates however, are based on the assumption that the model is well-specified, an assumption that is violated in most practical applications, since the required knowledge is rarely available. As a result, the produced uncertainty estimates can become very misleading; for example the prediction intervals (PIs) produced for the 95% confidence level may cover much less than 95% of the true labels. To address this issue, this paper introduces an extension of GPR based on a Machine Learning framework called, Conformal Prediction (CP). This extension guarantees the production of PIs with the required coverage even when the model is completely misspecified. The proposed approach combines the advantages of GPR with the valid coverage guarantee of CP, while the performed experimental results demonstrate its superiority over existing methods

中文翻译：

---

高斯过程回归的保证覆盖预测区间


高斯过程回归 (GPR) 是一种流行的回归方法，与大多数机器学习技术不同，它提供了预测的不确定性估计。然而，这些不确定性估计是基于模型是明确指定的假设，但在大多数实际应用中都违反了这一假设，因为所需的知识很少可用。因此，产生的不确定性估计可能会变得非常具有误导性；例如，为 95% 置信水平生成的预测区间 (PI) 可能覆盖远小于 95% 的真实标签。为了解决这个问题，本文介绍了基于机器学习框架的 GPR 扩展，称为保形预测（CP）。即使模型完全错误指定，此扩展也能保证生成具有所需覆盖范围的 PI。该方法结合了探地雷达的优点和CP的有效覆盖保证，而实验结果表明其优于现有方法