Sparse Variational Information Bottleneck Gaussian Processes for Uncertainty Estimation

Inducing-point-based sparse variational approximation scales Gaussian process models to large datasets but tends to overestimate observation noise and underestimate posterior variance. Parametric predictive Gaussian process regressor (PPGPR) improve on point-wise uncertainty estimations, especially for heteroskedastic data, by repairing an mismatch between the training loss and the predictive metric for sparse variational Gaussian process (SVGP). In this paper, we approach uncertainty estimation