We introduce Latent Gaussian Process Regression which is a latent variable extension allowing
modelling of non-stationary multi-modal processes using GPs. The approach is built on …

… For example, our algorithm is immediately applicable for training Gaussian process models
… same Gaussian process prior which is evaluated at the inputs X. Since X is a latent variable, …

… learning scenarios such as regression, classification, clustering. In this section, we detailed
the formulation of Gaussian Process Regression (GPR) to demonstrate the use of GP. …

… 3.1 Sparse Gaussian Process Regression We consider the standard Gaussian process
regression … The observations consist of the latent function values {F1,...,Fn} corrupted by some iid …

… -based regression can even be problematic for prediction in real-time and, thus, it is an
essential component of the LGP that it results in a substantial reduction of prediction latency …

… for the sparse Gaussian process (GP) regression and Gaussian process latent variable model
(… We will assume prior knowledge of Gaussian processes and variational inference, but we …

… In this work, we discuss the properties of a Gaussian process regression model with … latent
function f, which is given a Gaussian process prior. This implies that any finite subset of latent …

… We introduce a latent Gaussian process model for such data, establishing a connection
to functional data analysis. The functional methods proposed are non-parametric and …

… a new regression framework, Gaussian process regression … the nonparametric flexibility of
Gaussian processes. This model … the latent Gaussian processes in f(x) have additive Gaussian …

… learn a latent … latent space, but are generally deterministic and may not generalize well with
limited training data. We introduce a method for Gaussian Process Classification using latent …