Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification. However, they suffer from the curse of dimensionality whenever the number of variables increases. This challenge is generally addressed by assuming additional structure in theproblem, the preferred options being either additivity or low intrinsic dimensionality. Our contribution for high-dimensional Gaussian process modeling is to combine them with a multi-fidelity strategy, showcasing the advantages through experiments on synthetic functions and datasets.

翻译：高斯过程因其良好的预测精度、解析可处理性以及内置的不确定性量化能力，已成为回归与分类任务中广泛采用的技术。然而，当变量数量增加时，它们会受到维数灾难的影响。通常通过假设问题具有额外结构来应对这一挑战，首选方案是可加性或低内在维度。我们针对高维高斯过程建模的贡献在于，将这两种方法与多保真策略相结合，并通过合成函数与数据集上的实验展示了其优势。