It is suggested that asymptotically finite information gain may be an important characteristic of good query algorithms, in which a committee of students is trained on the same data set.Expand

An approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets is developed based on a combination of a Bayesian on-line algorithm and a sequential construction of a relevant subsample of data that fully specifies the prediction of the GP model.Expand

The relationship between the Laplace and the variational approximation is discussed, and it is shown that for models with gaussian priors and factorizing likelihoods, the number of variational parameters is actually .Expand

This work develops an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets by using an appealing parametrisation and projection techniques that use the RKHS norm.Expand

The theoretical foundations of advanced mean field methods are covered, the relation between the different approaches are explored, the quality of the approximation obtained is examined, and their application to various areas of probabilistic modeling is demonstrated.Expand

A mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems is derived and an approximate leave-one-out estimator for the generalization error is computed.Expand

A class of non-linear stochastic optimal control problems introduced by Todorov is reformulate as a Kullback-Leibler (KL) minimization problem and it is shown how this KL control theory contains the path integral control method as a special case.Expand

A novel framework for approximations to intractable probabilistic models which is based on a free energy formulation is proposed which requires two tractable probability distributions which are made consistent on a set of moments and encode different features of the originalintractable distribution.Expand

Assume {P θ : θ ∈ Θ} is a set of probability distributions with a common dominating measure on a complete separable metric space Y. A state θ * ∈Θ is chosen by Nature. A statistician obtains n… Expand

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a… Expand