Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable approximation error. Recent work accounts for this error in the form of computational uncertainty, which enables -- at the cost of quadratic complexity -- an explicit tradeoff between computation and precision. Here we extend this development to model selection, which requires significant enhancements to the existing approach, including linear-time scaling in the size of the dataset. We propose a novel training loss for hyperparameter optimization and demonstrate empirically that the resulting method can outperform SGPR, CGGP and SVGP, state-of-the-art methods for GP model selection, on medium to large-scale datasets. Our experiments show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty -- a fundamental prerequisite for optimal decision-making.

本研究解决了高斯过程模型选择在训练数据集规模扩大时所带来的时间和内存问题。提出了一种新的超参数优化训练损失，能够实现线性时间缩放，加速模型选择过程，并在中到大规模数据集上超越现有的最先进方法。研究结果表明，计算感知高斯过程可以在不显著妨碍不确定性量化能力的情况下，成功训练大规模数据集，为最佳决策提供了基础。

计算感知高斯过程：模型选择与线性时间推理