Add to ORKGA number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nyström method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nyström method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nyström approximation would be expected to provide a good approximation to exact GP regression
Gaussian process (GP) prediction suffers from O(n^3) scaling with the data set size n. By using a f...
Gaussian Process Regression is a non parametric approach for estimating relationships in data sets. ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...
A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including t...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
This paper considers the quantification of the prediction performance in Gaussian process regression...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
This report tends to provide details on how to perform predictions using Gaussian process regression...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as k...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
This paper modify the basic Gaussian process regression predictions to get the correct order of magn...
Gaussian process (GP) prediction suffers from O(n^3) scaling with the data set size n. By using a f...
Gaussian Process Regression is a non parametric approach for estimating relationships in data sets. ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...
A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including t...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine l...
This paper considers the quantification of the prediction performance in Gaussian process regression...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
This report tends to provide details on how to perform predictions using Gaussian process regression...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as k...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
This paper modify the basic Gaussian process regression predictions to get the correct order of magn...
Gaussian process (GP) prediction suffers from O(n^3) scaling with the data set size n. By using a f...
Gaussian Process Regression is a non parametric approach for estimating relationships in data sets. ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...