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Publikationsliste

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2018

Approximate Bayes learning of stochastic differential equations [20]

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

Phys. Rev. E, 022109. 2018

Download Bibtex Eintrag [21]

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes [22]

Donner, C. and Opper, M.

Journal of Machine Learning Research, 1-34. 2018

Link zur Publikation [23] Download Bibtex Eintrag [24]

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed–Form Approixmation [25]

Harel, Y., Meir, R. and Opper, M.

Neural Computation, 2056–2112. 2018

Download Bibtex Eintrag [26]

Efficient Bayesian Inference for a Gaussian Process Density Model [27]

Donner, C. and Opper, M.

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

Download Bibtex Eintrag [28]

Expectation Propagation for Approximate Inference: Free Probability Framework [29]

Cakmak, B. and Opper, M.

Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 1276-1280. 2018

Download Bibtex Eintrag [30]

2017

Inverse Ising problem in continuous time: A latent variable approach [31]

Donner, C. and Opper, M.

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

Link zur Publikation [32] Download Bibtex Eintrag [33]

Inferring hidden states in Langevin dynamics on large networks: Average case performance [34]

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

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

Download Bibtex Eintrag [35]

Dynamical Functional Theory for Compressed Sensing [36]

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

2017 IEEE International Symposium on Information Theory (ISIT). IEEE Press, 2143-2147. 2017

Download Bibtex Eintrag [37]

Perturbative Black Box Corrected Variational Inference [38]

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

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

Link zur Publikation [39] Download Bibtex Eintrag [40]

An Estimator for the Relative Entropy Rate of Path Measures for Stochastic Differential Equations [41]

M., O.

J. Comput. Phys.. Academic Press Professional, Inc., 127–133. 2017

Download Bibtex Eintrag [42]

A statistical physics approach to learning curves for the inverse Ising problem [43]

Bachschmid-Romano, L. and Opper, M.

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

Link zur Publikation [44] Download Bibtex Eintrag [45]

2016

Expectation propagation for continuous time stochastic processes [46]

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

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

Link zur Publikation [47] Download Bibtex Eintrag [48]

Visualizing the effects of a changing distance on data using continuous embeddings [49]

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

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

Link zur Publikation [50] Download Bibtex Eintrag [51]

Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model [52]

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

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

Link zur Publikation [53] Download Bibtex Eintrag [54]

Variational estimation of the drift for stochastic differential equations from the empirical density [55]

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

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

Link zur Publikation [56] Download Bibtex Eintrag [57]

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