Joaquin Quiñonero C.

PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS — APPLICATION TO MULTIPLE-STEP AHEAD FORECASTING (2009)

Joaquin Quiñonero C, Agathe Girard, Jan Larsen, Carl Edward Rasmussen

The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably...

Multi-class Semi-supervised Learning With The ǫ-truncated Multinomial Probit Gaussian Process (2008)

Simon Rogers, Mark Girolami, D. Lawrence, Anton Schwaighofer, Joaquin Quiñonero C

Recently, the null category noise model has been proposed as a simple and elegant solution to the problem of incorporating unlabeled data into a Gaussian process (GP) classification model. In this...

PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS — APPLICATION TO MULTIPLE-STEP AHEAD FORECASTING (2008)

Joaquin Quiñonero C, Agathe Girard, Jan Larsen, Carl Edward Rasmussen

The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably...

Gaussian process approximations of stochastic differential equations (2007)

Cédric Archambeau, Dan Cornford, D. Lawrence, Anton Schwaighofer, Joaquin Quiñonero C

Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior...

Learning depth from stereo (2004)

Fabian H. Sınz, Joaquin Quiñonero C, Gökhan H. Bakır, Carl E. Rasmussen, Matthıas O. Franz

Abstract. We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach...