论文标题
在稀疏的高斯工艺中选择结的结节，具有变异目标函数
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function
论文作者
论文摘要
基于稀疏的基于结的高斯工艺取得了相当大的成功，这是对完整高斯流程的可扩展近似值。某些稀疏模型可以通过对真实后部的特定变异近似来得出，并且可以选择结，以最大程度地减少近似和真实后端之间的kullback-leibler差异。尽管这是一种成功的方法，但由于要优化的参数数量，因此同时优化结的速度可能会很慢。此外，几乎没有提出的选择结数的方法，文献中没有实验结果。我们建议使用基于贝叶斯优化的一次性结选择算法来选择结的数量和位置。我们展示了这种方法相对于在三个基准数据集上同时优化结的竞争性能，但以计算成本的一小部分。
Sparse, knot-based Gaussian processes have enjoyed considerable success as scalable approximations to 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 simultaneous optimization of knots on three benchmark data sets, but at a fraction of the computational cost.
