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List of Publications

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O

Nichtlineare Anregungen als Quasiteilchen in der Toda–Kette (Nonlinear excitations as quasiparticles in the Toda chain) [21]

Opper, M.

1983

Download Bibtex entry [22]

Eine neue Methode zur Lösung eindimensionaler Modelle mit Unordnung (A new method for solving one dimensional models with disorder) [23]

Opper, M.

1987 Giessen University, Germany

Download Bibtex entry [24]

Approximate inference in continuous time Gaussian-Jump processes [25]

Opper, M., Ruttor, A. and Sanguinetti, G.

Advances in Neural Information Processing Systems 23, 1831–1839. 2010

Link to publication [26] Download Bibtex entry [27]

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster [28]

Opper, M., Dewar, M. A., Kadirkamanathan, V. and Sanguinetti, G.

BMC Systems Biology. BMC Systems Biology %, 669 - 676. 2010

Download Bibtex entry [29]

A new variational radial basis function approximation for inference in multivariate diffusions [30]

Opper, M., Vrettas, M. D. and Cornford, D.

Neurocomputing. Elsevier, 1186 - 1198. 2010

Download Bibtex entry [31]

Learning combinatorial transcriptional dynamics from gene expression data [32]

Opper, M. and Sanguinetti, G.

Bioinformatics. Oxford Journals, 1623-1629. 2010

Download Bibtex entry [33]

A theory of solving TAP equations for Ising models with general invariant random matrices [34]

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

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

Link to publication [35] Download Bibtex entry [36]

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models [37]

Opper, M., Paquet, U. and Winther, O.

Journal of Machine Learning Research, 2857-2898. 2013

Link to publication [38] Download Bibtex entry [39]

Expectation Consistent Approximate Inference [40]

Opper, M. and Winther, O.

Journal of Machine Learning Research, 2177 - 2204. 2005

Link to publication [41] Download Bibtex entry [42]

Tractable approximations for probabilistic models: The adaptive TAP mean field approach [43]

Opper, M. and Winther, O.

Phys. Rev. Lett., 3695-3699. 2001

Link to publication [44] Download Bibtex entry [45]

Adaptive and Selfaveraging TAP Mean Field Theory [46]

Opper, M. and Winther, O.

Physical Review E, 056131. 2001

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

Universal learning curves of support vector machines [49]

Opper, M. and Urbanczik, R.

Phys. Rev. Lett., 4410-4413. 2001

Link to publication [50] Download Bibtex entry [51]

Probabilistic data modelling with adaptive TAP mean field theory [52]

Opper, M. and Winther, O.

Second Minerva Workshop on Frontiers in the Physics of Complex Systems, 119-125. 2001

Link to publication [53] Download Bibtex entry [54]

Support Vector machines learning noisy polynomial rules [55]

Opper, M. and Urbanczik, R.

Second Minerva Workshop on Frontiers in the Physics of Complex Systems, 110-118. 2001

Link to publication [56] Download Bibtex entry [57]

Gaussian Processes for Classification: Mean Field Algorithms [58]

Opper, M. and Winther, O.

Neural Computation, 2655-2684. 2000

Link to publication [59] Download Bibtex entry [60]
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