Gaussian Processes (GP) have become popular machine learning methods for
kernel based learning on datasets with complicated covariance structures. In
this paper, we present a novel extension to the GP framework using a
contaminated normal likelihood function to better account for heteroscedastic
variance and outlier noise. We propose a scalable inference algorithm based on
the Sparse Variational Gaussian Process (SVGP) method for fitting sparse
Gaussian process regression models with contaminated normal noise on large
datasets. We examine an application to geomagnetic ground perturbations, where
the state-of-art prediction model is based on neural networks. We show that our
approach yields shorter predictions intervals for similar coverage and accuracy
when compared to an artificial dense neural network baseline.

本文提出了一种使用受污染正态似然函数扩展高斯过程框架的方法，以更好地考虑异方差性和异常噪声。我们还提出了一种基于稀疏变分高斯过程方法的可扩展推理算法，用于在大型数据集上拟合具有受污染正态噪声的稀疏高斯过程回归模型。我们在地磁地面扰动应用中进行了实证研究，该应用基于神经网络的最先进预测模型，结果表明与人工密集神经网络基线相比，我们的方法在保持相似的覆盖率和准确性的情况下获得了较短的预测区间。