Abstract Gaussian Processes (GPs) are powerful kernelized methods for non-parametric
regression used in many applications. However, their use is limited to a few thousand of …

We provide a new unifying view, including all existing proper probabilistic sparse
approximations for Gaussian process regression. Our approach relies on expressing the …

To handle massive data, a variety of sparse Gaussian Process (GP) methods have been
proposed to reduce the computational cost. Many of them essentially map the large dataset …

In this paper we introduce a novel model for Gaussian process (GP) regression in the fully
Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive …

Gaussian processes (GPs) are an important tool in machine learning and statistics.
However, off-the-shelf GP inference procedures are limited to datasets with several …

Recent work on scaling up Gaussian process regression (GPR) to large datasets has
primarily focused on sparse GPR, which leverages a small set of basis functions to …

Gaussian processes are widely used in nonparametric regression, classification and
spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties …

Most existing sparse Gaussian process (gp) models seek computational advantages by
basing their computations on a set of m basis functions that are the covariance function of …

We present a method for the sparse greedy approximation of Bayesian Gaussian process
regression, featuring a novel heuristic for very fast forward selection. Our method is …

We propose an efficient discrete optimization algorithm for selecting a subset of training data
to induce sparsity for Gaussian process regression. The algorithm estimates this inducing …