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List of Publications

Learning Rules and Learning Times in Neural Networks

Opper, M.

Dynamics of Networks. Akademie Verlag Berlin, 66-75. 1989

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Statistical Mechanics of Learning in Neural Network Models

Opper, M., Diederich, S. and Anlauf, J. K.

Chaos and Complexity. World Scientific, 219–223. 1987

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Gaussian Process Approximations of Stochastic Differential Equations

Archambeau, C., Cornford, D., Opper, M. and Shawe-Taylor, J.

Journal of Machine Learning Research: Workshop and Conference Proceedings, 1:1–16. 2007

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Expectation Consistent Free Energies for Approximate Inference

Opper, M. and Winther, O.

Advances in Neural Information Processing Systems 17 2005

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Approximate Analytical Bootstrap Averages for Support Vector Classifiers

Malzahn, D. and Opper, M.

Advances in Neural Information Processing Systems 16. MIT Press. 2004

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Variational Linear Response

Opper, M. and Winther, O.

Advances in Neural Information Processing Systems 16. MIT Press. 2004

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A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages

Malzahn, D. and Opper, M.

Advances in Neural Information Processing Systems 15. MIT Press, 327-334. 2003

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Data Assimilation with Sequential Gaussian Processes

Csató, L., Cornford, D. and Opper, M.

Uncertainty in geometrical computation, published by: Kluwer. Kluwer, 29-40. 2002

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Asymptotic Universality for Learning Curves of Support Vector Machines

Opper, M. and Urbanczik, R.

Advances in Neural Information Processing Systems 14. MIT Press, 479-486. 2002

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Approximate Gaussian process inference for the drift of stochastic differential equations

Ruttor, A., Batz, P. and Opper, M.

Advances in Neural Information Processing Systems, 2040-2048. 2013

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A Variational Approach to Learning Curves

Malzahn, D. and Opper, M.

Advances in Neural Information Processing Systems 14. MIT Press, 463-469. 2002

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TAP Gibbs Free Energy, Belief Propagation and Sparsity

Csató, L., Opper, M. and Winther, O.

Advances in Neural Information Processing Systems 14. MIT Press, 657-663. 2002

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