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Publikationsliste

2016

Expectation propagation for continuous time stochastic processes

Cseke, B., Schnoerr, D., Opper, M. and Sanguinetti, G.

Journal of Physics A: Mathematical and Theoretical. IOPscience, 494002. 2016

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Visualizing the effects of a changing distance on data using continuous embeddings

Gruenhage, G., Opper, M. and Barthelme, S.

Computational Statistics & Data Analysis. Elsevier, 51 - 65. 2016

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Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model

Romano, L. B., Battistin, C., Opper, M. and Roudi, Y.

Journal of Physics A: Mathematical and Theoretical. IOPscience, 434003. 2016

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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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Extended Plefka expansion for stochastic dynamics

Bravi, B., Sollich, P. and Opper, M.

Journal of Physics A: Mathematical and Theoretical, 194003. 2016

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A theory of solving TAP equations for Ising models with general invariant random matrices

Opper, M., Cakmak, B. and Winther, O.

Journal of Physics A: Mathematical and Theoretical, 114002. 2016

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2017

Dynamical Functional Theory for Compressed Sensing

Çakmak, B., Opper, M., Winther, O. and Fleury, B. H.

2017 IEEE International Symposium on Information Theory (ISIT). IEEE Press, 2143-2147. 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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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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Inferring hidden states in Langevin dynamics on large networks: Average case performance

Bravi, B., Opper, M. and Sollich, P.

Phys. Rev. E. American Physical Society, 012122. 2017

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Inverse Ising problem in continuous time: A latent variable approach

Donner, C. and Opper, M.

Phys. Rev. E. American Physical Society, 062104. 2017

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2018

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Donner, C. and Opper, M.

Journal of Machine Learning Research, 1-34. 2018

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Efficient Bayesian Inference for a Gaussian Process Density Model

Donner, C. and Opper, M.

Proceedings of UAI 2018. AUAI Press, 53-62. 2018

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