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A

Variational Inference for Diffusion Processes

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

Advances in Neural Information Processing Systems 20. MIT Press, 17–24. 2008

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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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Approximate inference for continuous–time Markov processes

Archambeau, C. and Opper, M.

Bayesian Time Series Models. Cambridge University Press, 125–140.. 2011

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The Variational Gaussian Approximation Revisited

Archambeau, C. and Opper, M.

Neural Computation, 786-92. 2009

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B

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves

Bachschmid-Romano, L. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing, P09016. 2015

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A statistical physics approach to learning curves for the inverse Ising problem

Bachschmid-Romano, L. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, 063406. 2017

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Perturbative Black Box Corrected Variational Inference

Bamler R., C. Z. O. M. M. S.

Advances in Neural Information Processing Systems 30. IEEE, 11. 2017

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Variational estimation of the drift for stochastic differential equations from the empirical density

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

Journal of Statistical Mechanics: Theory and Experiment, 083404. 2016

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Approximate Bayes learning of stochastic differential equations

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

Phys. Rev. E, 022109. 2018

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Construction Algorithm for the Parity Machine

Biehl, M. and Opper, M.

Physica A, 307–313. 1993

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Tilinglike Learning in the Parity Machine

Biehl, M. and Opper, M.

Phys. Rev. A, 6888–6894. 1991

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