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2016

Expectation propagation for continuous time stochastic processes [21]

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

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

Link to publication [22] Download Bibtex entry [23]

Visualizing the effects of a changing distance on data using continuous embeddings [24]

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

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

Link to publication [25] Download Bibtex entry [26]

Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model [27]

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

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

Link to publication [28] Download Bibtex entry [29]

Variational estimation of the drift for stochastic differential equations from the empirical density [30]

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

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

Link to publication [31] Download Bibtex entry [32]

Extended Plefka expansion for stochastic dynamics [33]

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

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

Link to publication [34] Download Bibtex entry [35]

A theory of solving TAP equations for Ising models with general invariant random matrices [36]

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

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

Link to publication [37] Download Bibtex entry [38]

2017

Dynamical Functional Theory for Compressed Sensing [39]

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 entry [40]

Perturbative Black Box Corrected Variational Inference [41]

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

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

Link to publication [42] Download Bibtex entry [43]

An Estimator for the Relative Entropy Rate of Path Measures for Stochastic Differential Equations [44]

M., O.

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

Download Bibtex entry [45]

A statistical physics approach to learning curves for the inverse Ising problem [46]

Bachschmid-Romano, L. and Opper, M.

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

Link to publication [47] Download Bibtex entry [48]

Inferring hidden states in Langevin dynamics on large networks: Average case performance [49]

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

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

Download Bibtex entry [50]

Inverse Ising problem in continuous time: A latent variable approach [51]

Donner, C. and Opper, M.

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

Link to publication [52] Download Bibtex entry [53]

2018

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes [54]

Donner, C. and Opper, M.

Journal of Machine Learning Research, 1-34. 2018

Link to publication [55] Download Bibtex entry [56]

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed–Form Approixmation [57]

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

Neural Computation, 2056–2112. 2018

Download Bibtex entry [58]

Efficient Bayesian Inference for a Gaussian Process Density Model [59]

Donner, C. and Opper, M.

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

Download Bibtex entry [60]

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[19] https://www.ki.tu-berlin.de/menue/publications/par
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[22] http://stacks.iop.org/1751-8121/49/i=49/a=494002

[23] https://www.ki.tu-berlin.de/menue/publications/par
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[30] https://www.ki.tu-berlin.de/menue/publications/par
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[31] http://stacks.iop.org/1742-5468/2016/i=8/a=083404

[32] https://www.ki.tu-berlin.de/menue/publications/par
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[33] https://www.ki.tu-berlin.de/menue/publications/par
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[34] http://stacks.iop.org/1751-8121/49/i=19/a=194003

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[36] https://www.ki.tu-berlin.de/menue/publications/par
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[37] http://stacks.iop.org/1751-8121/49/i=11/a=114002

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[39] https://www.ki.tu-berlin.de/menue/publications/par
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[42] https://arxiv.org/pdf/1709.07433.pdf

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[47] http://stacks.iop.org/1742-5468/2017/i=6/a=063406

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[49] https://www.ki.tu-berlin.de/menue/publications/par
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[50] https://www.ki.tu-berlin.de/menue/publications/par
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[51] https://www.ki.tu-berlin.de/menue/publications/par
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[52] https://link.aps.org/doi/10.1103/PhysRevE.96.06210
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[54] https://www.ki.tu-berlin.de/menue/publications/par
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[55] http://jmlr.org/papers/v19/17-759.html

[56] https://www.ki.tu-berlin.de/menue/publications/par
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[57] https://www.ki.tu-berlin.de/menue/publications/par
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[58] https://www.ki.tu-berlin.de/menue/publications/par
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[59] https://www.ki.tu-berlin.de/menue/publications/par
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[76] https://www.ki.tu-berlin.de/menue/publications/par
ameter/en/?showp=16&tx_sibibtex_pi1%5Bsort%5D=year%
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[77] https://www.ki.tu-berlin.de/menue/publications/par
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3A0&cHash=a96852de2e3a44fb51f5025afe1d8583

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List of Publications

2016

Expectation propagation for continuous time stochastic processes [21]

Visualizing the effects of a changing distance on data using continuous embeddings [24]

Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model [27]

Variational estimation of the drift for stochastic differential equations from the empirical density [30]

Extended Plefka expansion for stochastic dynamics [33]

A theory of solving TAP equations for Ising models with general invariant random matrices [36]

2017

Dynamical Functional Theory for Compressed Sensing [39]

Perturbative Black Box Corrected Variational Inference [41]

An Estimator for the Relative Entropy Rate of Path Measures for Stochastic Differential Equations [44]

A statistical physics approach to learning curves for the inverse Ising problem [46]

Inferring hidden states in Langevin dynamics on large networks: Average case performance [49]

Inverse Ising problem in continuous time: A latent variable approach [51]

2018

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes [54]

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed–Form Approixmation [57]

Efficient Bayesian Inference for a Gaussian Process Density Model [59]

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List of Publications

2016

Expectation propagation for continuous time stochastic processes [21]

Visualizing the effects of a changing distance on data using continuous embeddings [24]

Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model [27]

Variational estimation of the drift for stochastic differential equations from the empirical density [30]

Extended Plefka expansion for stochastic dynamics [33]

A theory of solving TAP equations for Ising models with general invariant random matrices [36]

2017

Dynamical Functional Theory for Compressed Sensing [39]

Perturbative Black Box Corrected Variational Inference [41]

An Estimator for the Relative Entropy Rate of Path Measures for Stochastic Differential Equations [44]

A statistical physics approach to learning curves for the inverse Ising problem [46]

Inferring hidden states in Langevin dynamics on large networks: Average case performance [49]

Inverse Ising problem in continuous time: A latent variable approach [51]

2018

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes [54]

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed–Form Approixmation [57]

Efficient Bayesian Inference for a Gaussian Process Density Model [59]

Zusatzinformationen / Extras

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