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-the-art prediction model is based on neural networks. We show that our approach yields shorter prediction intervals for similar coverage and accuracy when compared to an artificial dense neural network baseline.

翻译：高斯过程（GP）已成为对具有复杂协方差结构的数据集进行基于核学习的流行机器学习方法。本文提出了一种基于污染正态似然函数的GP框架新扩展，以更好地处理异方差方差和离群噪声。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。我们研究了该方法在地面地磁扰动预测中的应用，该领域当前最先进的预测模型基于神经网络。结果表明，与人工密集神经网络基线相比，我们的方法在覆盖率和准确度相近的情况下能产生更短的预测区间。