We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points which can lead to more scalable algorithms than previous methods. It is based on decomposing a Gaussian process as a sum of two independent processes: one in the subspace spanned by the inducing basis and the other in the orthogonal complement to this subspace. We show that this formulation recovers existing approximations and at the same time allows to obtain tighter lower bounds on the marginal likelihood and new stochastic variational inference algorithms. We demonstrate the efficiency of these algorithms in several Gaussian process models ranging from standard regression to multi-class classification using (deep) convolutional Gaussian processes and report state-of-the-art results on CIFAR-10 with purely GP-based models.

本文介绍了一种新的稀疏变分逼近高斯过程的解释，使用感应点可以比以前的方法更具有可扩展性。它基于将高斯过程分解为两个独立过程的和： 一个由有限势基感应点并跨越另一个捕获其余变化。我们表示，这种表达重新获得了现有的逼近值，并且同时允许获得较紧的较低边界和新的随机变分推理算法。我们展示了这些算法的效率，从标准回归到使用（深入）卷积高斯过程的多类分类，并在CIFAR-10中报告了完全基于GP的模型的最新结果。

高斯过程的稀疏正交变分推断