Source coding theorems and Shannon rate-distortion functions were studied for the discrete-time Wiener process by Berger and generalized to nonstationary Gaussian autoregressive p...
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using an input-dependent adaptation of the ...
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process pers...
Mauricio Alvarez, David Luengo, Michalis Titsias, ...
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain ...
Shrihari Vasudevan, Fabio T. Ramos, Eric Nettleton...