Add to ORKGWe provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints
We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the th...
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Good sparse approximations are essential for practical inference in Gaussian Processes as the comput...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Gaussian processes are powerful nonparametric distributions over continuous functions that have beco...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
In recent years there has been an increased interest in applying non-parametric methods to real-worl...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to over...
We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the th...
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
We provide a new unifying view, including all existing proper probabilistic sparse approximations fo...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Good sparse approximations are essential for practical inference in Gaussian Processes as the comput...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Gaussian processes are powerful nonparametric distributions over continuous functions that have beco...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
In recent years there has been an increased interest in applying non-parametric methods to real-worl...
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems ...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to over...
We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the th...
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has...