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.

翻译：高斯过程（GP）已成为处理具有复杂协方差结构数据集的核方法学习中流行的机器学习方法。本文提出了一种基于污染正态似然函数的高斯过程框架新型扩展方法，以更有效地处理异方差方差和异常值噪声。我们提出了一种基于稀疏变分高斯过程（SVGP）方法的可扩展推理算法，用于在大型数据集上拟合具有污染正态噪声的稀疏高斯过程回归模型。我们将其应用于地磁地面扰动预测，该领域当前最先进的预测模型基于神经网络。研究结果表明，与人工密集神经网络基线方法相比，我们的方法在相似覆盖率和精度条件下能够产生更短的预测区间。