Sparse, knot‐based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback‐Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one‐at‐a‐time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost.

中文翻译：

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具有变分目标函数的稀疏高斯过程中的结选择。


作为完整高斯过程的可扩展近似，稀疏、基于结的高斯过程已经取得了相当大的成功。某些稀疏模型可以通过对真实后验的特定变分近似来导出，并且可以选择结来最小化近似后验和真实后验之间的 Kullback-Leibler 散度。虽然这是一种成功的方法，但由于要优化的参数数量，节点的同步优化可能会很慢。此外，很少有人提出选择结数的方法，文献中也没有实验结果。我们建议使用基于贝叶斯优化的一次一个结选择算法来选择结的数量和位置。我们展示了该方法相对于在三个基准数据集上同时优化结的竞争性能，但计算成本仅为其一小部分。