Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression

no code implementations • 5 Dec 2019 • Tong Teng, Jie Chen, Yehong Zhang, Kian Hsiang Low

To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SGPR models where its posterior belief is learned together with that of the other variational variables (i. e., inducing variables and kernel hyperparameters).

regression Stochastic Optimization +1