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2003 | |||

Beal, M.J. | Variational Algorithms for Approximate Bayesian Inference | pdf html | PhD. Thesis |

Beal, M.J. and Ghahramani, Z. | The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures | pdf ps.gz | Bayesian Statistics 7, Oxford University Press, 2003. |

Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N. | Application of Variational Bayesian Approach to Speech Recognition | ps.gz pdf | NIPS 15 |

Winn, J. | Variational Message Passing and its Applications | pdf html | PhD. Thesis |

2002 | |||

Choudrey, R. and Roberts, S. | Variational Mixture of Bayesian Independent Component Analysers | ps.gz | Neural Computation |

Gao, J., Gunn, S. and Kandola, J. | Adapting Kernels by Variational Approach in SVM | Lecture Notes in Artificial Intelligence, Vol.2557 pp395-406 (2002) | |

Ueda, N. and Ghahramani, Z. | Bayesian Model Search for Mixture Models Based on Optimizing Variational Bounds | Neural Networks | |

2001 | |||

Girolami, M. | A Variational Method for Learning Sparse and Overcomplete Representations | abstract pdf.zip | Neural Computation 13(11):2517-2532 |

Ghahramani, Z. and Beal, M.J. | Propagation Algorithms for Variational Bayesian Learning | abstract pdf ps.gz poster software | NIPS 13 |

Minka, T.P. | Using lower bounds to approximate integrals | abstract ps.gz | |

Sato, M. | On-line model selection based on the variational Bayes | Neural Computation, 13(7), 1649-1681 | |

MacKay, D. J. C. | A Problem with Variational Free Energy Minimization | website | First published Sat 23/6/01 |

2000 | |||

Attias, H | A Variational Bayesian framework for graphical models | ps | NIPS 12 |

Bishop, C.M. and Tipping, M.E. | Variational Relevance Vector Machines | citeseer | |

Bishop, C.M. and Winn, J.M. | Non-linear Bayesian Image Modelling | Proc. European Conf. on Computer Vision. ECCV | |

Choudrey, R., Penny, W.D. and Roberts, S.J. | An Ensemble Learning Approach to Independent Component Analysis | ps | IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, Dec. 2000 |

Ghahramani, Z. and Beal, M.J. | Variational Inference for Bayesian Mixtures of Factor Analysers | abstract pdf ps.gz poster software | NIPS 12:449-455 |

Ghahramani, Z. and Beal, M.J. | Graphical Models and Variational Methods | abstract pdf ps.gz | Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket |

Jaakkola, T. and Jordan, M.I. | Bayesian parameter estimation via variational methods | ps ps.gz | Statistics and Computing, 10:25--37, 2000 |

Jaakkola, T. | Tutorial on variational approximation methods | ps.gz ps | Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket |

Lappalainen, H. and Miskin, J.W. | Ensemble Learning | ps.gz | Advances in Independent Component Analysis (Ed. by Girolami, M). Springer-Verlag Scientific Publishers |

MacKay, D.J.C. and Gibbs, M.N. | Variational Gaussian Process Classifiers | citeseer | IEEE Neural Networks |

Miskin, J.W. | Ensemble Learning for Independent Component Analysis | ps.gz | PhD. Thesis |

Storkey, A.J. | Dynamic Trees: A structured variational approach giving efficient propagation rules | pdf pdf.gz djvu | UAI 2000 |

Valpola, H. | Bayesian Ensemble Learning for Nonlinear Factor Analysis | ps.gz | PhD. Thesis, Helsinki University of Technology, Espoo. In Acta Polytechnica Scandinavica, Mathematics and Computing Series No. 108 |

Valpola, H. | Nonlinear Independent Component Analysis Using Ensemble Learning: Theory | ps.gz | Proc. of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki, Finland pp. 251-256 |

1999 | |||

Attias, H. | Inferring parameters and structure of latent variable models by variational Bayes | ps | UAI 15 |

Bishop, C.M. | Variational PCA | ICANN 9 | |

Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. | An introduction to variational methods for graphical models | ps.gz | Machine Learning 37:183-233 |

Lawrence, N.D. and Azzouzi, M. | A Variational Bayesian Committee of Neural Networks | citeseer | |

1998 & before | |||

Barber, D. and Bishop, C. M. (1998) | Ensemble learning for multi-layer networks | djvu | NIPS 10 |

MacKay, D.J.C. (1997) | Ensemble Learning for Hidden Markov Models | abstract pdf ps.gz | Unpublished manuscript |

Waterhouse, S., MacKay, D.J.C. and Robinson, T. (1996) | Bayesian methods for Mixtures of Experts | djvu | NIPS 8 |

MacKay, D.J.C. (1995) | Developments in Probabilistic Modelling with Neural Networks -- Ensemble Learning | ps and pdf | In Kappen, B. and Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the 3rd Annual Symposium on Neural Networks, Nijmegen, Netherlands, 14-15 September 1995 |

MacKay, D.J.C. (1995) | Ensemble Learning and Evidence Maximisation | abstract pdf ps.gz | Unpublished manuscript |

Hinton, G.E. and van Camp, D. (1993) | Keeping Neural Networks Simple by Minimizing the Description Length of the Weights | ps | COLT 6 |

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