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

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

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 method which allows for online updating of sparse Gaussian Process (GP)
regression algorithms for any set of inducing inputs. This method is derived both for the Fully …

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

Gaussian Processes are widely used for regression tasks. A known limitation in the
application of Gaussian Processes to regression tasks is that the computation of the solution …

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 …

Gaussian process regression allows a simple analytical treatment of exact Bayesian
inference and has been found to provide good performance, yet scales badly with the …

This paper proposes an approach for online training of a sparse multi-output Gaussian
process (GP) model using sequentially obtained data. The considered model combines …