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All publications by David Barber
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Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations
David Barber and Yali Wang
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
Thomas Furmston and David Barber
Advances in Neural Information Processing Systems 25, 2012


Affine Independent Variational Inference
Edward Challis and David Barber
Advances in Neural Information Processing Systems 25, 2012


Bayesian Conditional Cointegration
Chris Bracegirdle and David Barber
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Edward Challis and David Barber
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Switch-Reset Models : Exact and Approximate Inference
Chris Bracegirdle and David Barber
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Variational methods for Reinforcement Learning
Thomas Furmston and David Barber
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
David Barber and Silvia Chiappa
Advances in Neural Information Processing Systems 19, 2006


A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems
David Barber and Bertrand Mesot
Advances in Neural Information Processing Systems 19, 2006


Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
David Barber
Journal of Machine Learning Research, 2006


A graphical model for chord progressions embedded in a psychoacoustic space
Jean-françcois Paiement, Douglas Eck, Samy Bengio and David Barber
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Kernelized Infomax Clustering
David Barber and Felix V. Agakov
Advances in Neural Information Processing Systems 18, 2005


The IM Algorithm: A Variational Approach to Information Maximization
David Barber and Felix V. Agakov
Advances in Neural Information Processing Systems 16, 2003


Learning in Spiking Neural Assemblies
David Barber
Advances in Neural Information Processing Systems 15, 2002


Dynamic Bayesian Networks with Deterministic Latent Tables
David Barber
Advances in Neural Information Processing Systems 15, 2002