Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
This work addresses scalability for Gaussian process practitioners, offering a method that is incremental but improves computational efficiency and accuracy.
The paper tackles the scalability problem of Gaussian processes by introducing a new variational approximation using harmonic kernel decomposition, achieving state-of-the-art results on CIFAR-10 among pure GP models.
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.