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List of Publications

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Reinforcement learning with Gaussian Processes [21]

Seiler, J.

2012

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

Probabilistic and Genetic Attacks on the Key-Exchange Protocol Using Permutation Parity Machines [24]

Seoane, L. F.

2011

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

Query by committee [27]

Seung, H. S., Opper, M. and Sompolinsky, H.

Proceedings of the Fifth Annual Conference on Computational Learning Theory. ACM Press, 287-294. 1992

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

Variational Markov Chain Monte Carlo for Inference in Partially Observed Nonlinear Diffusions [30]

Shen, Y., Archambeau, C., Cornford, D. and Opper, M.

Proceedings of the Workshop Inference and Estimation in Probabilistic Time-Series Models, 67-78. 2008

Download Bibtex entry [31]

A comparison of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems [32]

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

Journal of Signal Processing Systems 2009

Link to publication [33] Download Bibtex entry [34]

Balancing a pole [35]

Sobierajski, K. and Sikora, P.

2011

Link to publication [36] Download Bibtex entry [37]

Efficient stochastic control with Kullback Leibler costs using kernel methods [38]

Sokoloski, S.

2013 TU Berlin

Link to publication [39] Download Bibtex entry [40]

MCMC for continuous time switching models [41]

Stimberg, F., Ruttor, A. and Opper, M.

NIPS Workshop on Monte Carlo Methods for Modern Applications (December 10, 2010, Whistler, Canada) 2010

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

Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling [44]

Stimberg, F., Ruttor, A. and Opper, M.

Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB). Tampere University of Technology, Tampere, Finland, 169–172. 2011

Link to publication [45] Download Bibtex entry [46]

Inference in continuous-time change-point models [47]

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

Advances in Neural Information Processing Systems 24, 2717–2725. 2011

Link to publication [48] Download Bibtex entry [49]

Bayesian Inference for Change Points in Dynamical Systems with Reusable States—a Chinese Restaurant Process Approach [50]

Stimberg, F., Opper, M. and Ruttor, A.

Proceedings of the International Conference on Artificial Intelligence and Statistics 2012

Link to publication [51] Download Bibtex entry [52]

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data [53]

Stimberg, F., Ruttor, A. and Opper, M.

Advances in Neural Information Processing Systems 27, 730-738. 2014

Link to publication [54] Download Bibtex entry [55]

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data - Supplementary material [56]

Stimberg, F., Ruttor, A. and Opper, M.

2014

Link to publication [57] Download Bibtex entry [58]

Comparing Markov Chain Monte Carlo Proposal Densities for Diffusion Processes [59]

Stimberg, F.

2010 TU Berlin

Link to publication [60] Download Bibtex entry [61]

Diffusion Approximation for Bayesian Inference on Chemical Reaction Systems [62]

Stimberg, F.

2009

Link to publication [63] Download Bibtex entry [64]
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