PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by David Barber

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Number of EPrints submitted by this user: 24

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Leraning
Jean-Pascal Pfister, Taro Toyoizumi, David Barber and Wulfram Gerstner
Neural Computation 2004.

Expectation Correction for an augmented class of Switching Linear Gaussian Models
David Barber
(2005) Technical Report. IDIAP Research Institute, Switzerland.

Expectation Correction for smoothing in Switching Linear Gaussian State Space models
David Barber
Journal of Machine Learning Research Volume 7, pp. 2515-2540, 2005. ISSN 1533-7928

Tagging of name records for genealogical data browsing
Mike Perrow and David Barber
In: Joint Conference on Digital Libraries, 11-15 June 2006, Chapel Hill, NOrth Carolina.

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems
David Barber and Bertrand Mesot
In: Advances in Neural Information Processing Systems NIPS 20, Dec 2006, Vancouver, Canada.

A Simple Alternative Derivation of the Expectation Correction Algorithm
David Barber and Bertrand Mesot
Signal Processing Letters Volume 16, Number 2, pp. 121-124, 2009.

Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices
David Barber
Uncertainty in Artificial Intelligence 2008.

Identifying Graph Clusters using Variational Inference and links to Covariance Parameterisation
David Barber
Royal Society Philosophical Transactions A 2008.

Inference and Estimation in Probabilistic Time-Series Models
David Barber and Silvia Chiappa
(2008) Proceedings of the Isaac Newton Institute for Mathematical Sciences . Newton Institute , Cambridge, UK .

Solving deterministic policy (PO)MPDs using Expectation-Maximisation and Antifreeze
David Barber and Tom Furmston
European Conference on Machine Learning (LEMIR workshop) pp. 50-64, 2009.

Identifying graph clusters using variational inference and links to covariance parametrization
David Barber
Philosophical Transactions of the Royal Society A Volume 367, Number 1906, pp. 4407-4426, 2009.

Variational methods for Reinforcement Learning
David Barber and Tom Furmston
In: AISTATS 2010, 13-15 May, 2010, Sardinia, Italy.

Graphical Models for Time Series
David Barber and Taylan Cemgil
Signal Processing Magazine Volume 27, Number 6, pp. 18-28, 2010.

Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Edward Challis and David Barber
In: AISTATS 2011, 11-13 April 2011, Fort Lauderdale.

Switch-Reset Models : Exact and Approximate Inference
Chris Bracegirdle and David Barber
In: AISTATS 2011, 11-13 April 2011, Fort Lauderdale.

Bayesian Time Series Models
David Barber, Taylan Cemgil and Silvia Chiappa, ed. (2011) Cambridge University Press , Cambridge, UK . ISBN 0521196760

Bayesian Reasoning and Machine Learning
David Barber
(2011) Cambridge University Press , Cambridge, UK . ISBN ISBN-13: 9780521518147

Inference and estimation in probabilistic time series models
David Barber, Taylan Cemgil and Silvia Chiappa
In: Bayesian Time Series Models (2011) Cambridge University Press , Cambridge, UK . ISBN 13: 9780521196765

Approximate inference in switching linear dynamical systems using Gaussian mixtures
David Barber
In: Bayesian Time Series Models (2011) Cambridge University Press , Cambridge, UK . ISBN 13: 9780521196765

A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
Tom Furmston and David Barber
In: NIPS 2012, Dec 2012, USA.

Affine Independent Variational Inference
Edward Challis and David Barber
NIPS 2012 2012.

Bayesian Cointegration
Chris Bracegirdle and David Barber
ICML 2012 2012.

On the computational complexity of stochastic controller optimization in POMDPs
Nikos Vlassis, Michael Littman and David Barber
ACM Transactions on Computation Theory 2012.

Variational Optimisation
Joe Staines and David Barber
ESANN 2013 2013.