For large data sets, performing Gaussian process regression is computationally demanding
or even intractable. If data can be processed sequentially, the recursive regression method …

Two approaches for on-line Gaussian process regression with low computational and
memory demands are proposed. The first approach assumes known hyperparameters and …

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 …

Gaussian process regression is a Bayesian nonparametric regression model. Although the
Gaussian process regression has shown good performance in various experiments, it …

Gaussian Processes (GPs) are Bayesian non-parametric models that achieve state-of-the-
art performance in regression tasks. To allow for analytical tractability, noise power is usually …

One of the most current challenging problems in Gaussian process regression (GPR) is to
handle large-scale datasets and to accommodate an online learning setting where data …

Gaussian processes are usually parameterised in terms of their covariance functions.
However this makes it difficult to deal with multiple outputs, because ensuring that the …

Gaussian Processes (GPs) are state-of-the-art tools for regression. Inference of GP
hyperparameters is typically done by maximizing the marginal log-likelihood (ML). If the data …

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

Regression problems on massive data sets are ubiquitous in many application domains
including the Internet, earth and space sciences, and finances. In many cases, regression …