We consider the problem of calculating learning curves (i.e., average generalization performance) of Gaussian processes used for regression. On the basis of a simple expression for the generalization error, in terms of the eigenvalue decomposition of the covariance function, we derive a number of approximation schemes
We combine the replica approach from statistical physics with a variational approach to analyze lear...
This paper deals with the speed of convergence of the learning curve in a Gaussian process regressio...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Note: This paper is an extended version of the manuscript Learning curves for Gaussian process regr...
Based on a statistical mechanics approach, we develop a method for approximately computing average c...
International audienceThis paper deals with the learning curve in a Gaussian process regression fram...
We study the average case performance of multi-task Gaussian process (GP) re-gression as captured in...
We combine the replica approach from statistical physics with a varia-tional approach to analyze lea...
In this paper we introduce and illustrate non-trivial upper and lower bounds on the learning curves ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based...
The assessment of the reliability of systems which learn from data is a key issue to investigate tho...
Learning curves for Gaussian process regression are well understood when the 'student ' mo...
We study learning curves for Gaussian process regression which characterise per-formance in terms of...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
We combine the replica approach from statistical physics with a variational approach to analyze lear...
This paper deals with the speed of convergence of the learning curve in a Gaussian process regressio...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...
Note: This paper is an extended version of the manuscript Learning curves for Gaussian process regr...
Based on a statistical mechanics approach, we develop a method for approximately computing average c...
International audienceThis paper deals with the learning curve in a Gaussian process regression fram...
We study the average case performance of multi-task Gaussian process (GP) re-gression as captured in...
We combine the replica approach from statistical physics with a varia-tional approach to analyze lea...
In this paper we introduce and illustrate non-trivial upper and lower bounds on the learning curves ...
A wealth of computationally efficient approximation methods for Gaussian process regression have bee...
This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based...
The assessment of the reliability of systems which learn from data is a key issue to investigate tho...
Learning curves for Gaussian process regression are well understood when the 'student ' mo...
We study learning curves for Gaussian process regression which characterise per-formance in terms of...
Gaussian process (GP) is a stochastic process that has been studied for a long time and gained wide ...
We combine the replica approach from statistical physics with a variational approach to analyze lear...
This paper deals with the speed of convergence of the learning curve in a Gaussian process regressio...
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performan...