结合可加性与活动子空间的高维高斯过程建模
Combining additivity and active subspaces for high-dimensional Gaussian process modeling
AI总结：
针对高维高斯过程建模中的维度灾难，提出结合可加性与活动子空间，并引入多保真度策略，实验验证了其优势。
摘要：
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，是回归和分类中广泛采用的技术。然而，当变量数量增加时，它们会遭受维度灾难。这一挑战通常通过假设问题中存在额外结构来解决，首选方案要么是可加性，要么是低内在维度。我们对高维高斯过程建模的贡献是将它们与多保真度策略相结合，通过在合成函数和数据集上的实验展示了其优势。
英文摘要：
Gaussian processes are a widely embraced technique for regression 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 the problem, the preferred options being either additivity or low intrinsic dimensionality. After discussing their relative merits, our contribution for high-dimensional Gaussian process modeling is to combine them with a multi-fidelity strategy. We detail the corresponding construction and showcase the advantages through experiments on synthetic functions and datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
