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（Gaussian Processes, GP）已成为处理具有复杂协方差结构数据集上基于核学习的流行机器学习方法。本文提出了一种高斯过程框架的新扩展，通过使用污染正态似然函数来更好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（Sparse Variational Gaussian Process, 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的预测，该领域现有最优预测模型基于神经网络。结果表明，与人工密集型神经网络基线相比，我们的方法在相似覆盖率和准确度下产生了更短的预测区间。