We introduce a new scalable variational Gaussian process approximation which
provides a high fidelity approximation while retaining general applicability.
We propose the harmonic kernel decomposition (HKD), which uses Fourier series
to decompose a kernel as a sum of orthogonal kernels. Our variational
approximation exploits this orthogonality to enable a large number of inducing
points at a low computational cost. We demonstrate that, on a range of
regression and classification problems, our approach can exploit input space
symmetries such as translations and reflections, and it significantly
outperforms standard variational methods in scalability and accuracy. Notably,
our approach achieves state-of-the-art results on CIFAR-10 among pure GP
models.

本文提出了一种新的可扩展的变分高斯过程近似方法，采用傅里叶级数将核作为正交核的和进行分解，利用这种正交性使众多诱导点具有低计算成本，能在回归和分类问题中利用输入空间的对称性，明显优于标准变分方法，并在 CIFAR-10 中取得与纯 GP 模型的最新成果。