Fast Inference in Non-Conjugate Gaussian Process Models via Data Augmentation

We present AugmentedGaussianProcesses.jl, a software package for augmented stochastic variational inference (ASVI) for Gaussian process models with non-conjugate likelihood functions. The idea of ASVI is to find an augmentation of the original GP model which renders the model conditionally conjugate and perform inference in the augmented model. We evaluate our method for three GP models (GP classification, robust GP regression and a Bayesian SVM model) and demonstrate that it is up to two orders of magnitude faster than the state-of-the-art.