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 spanned by a finite basis of inducing points and the other capturing the remaining variation. 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 among purely GP-based models.

翻译：我们提出一种基于诱导点的稀疏变分近似新解释方法，该方法可推导出比现有方法更具扩展性的算法。该解释将高斯过程分解为两个独立过程之和：一个由有限基诱导点张成，另一个捕获剩余变异。我们证明该公式既能恢复现有近似方法，又能获得边际似然的更紧下界及新的随机变分推断算法。我们通过从标准回归到多分类任务（采用深度卷积高斯过程）的多种高斯过程模型验证了这些算法的有效性，并在纯高斯过程模型框架下报告了CIFAR-10数据集上的最优结果。