Philipp Batz and Andreas Ruttor and Manfred Opper (2016). Variational estimation of the drift for stochastic differential equations from the empirical density. Journal of Statistical Mechanics: Theory and Experiment, 083404.

Andreas Ruttor and Philipp Batz and Manfred Opper (2013). Approximate Gaussian process inference for the drift of stochastic differential equations - Supplementary material.

Andreas Ruttor and Philipp Batz and Manfred Opper (2013). Approximate Gaussian process inference for the drift of stochastic differential equations. Advances in Neural Information Processing Systems, 2040-2048.

Sven Wiethölder and Andreas Ruttor and Uwe Bergemann and Manfred Opper and Adam Wolisz (2013). DARA: Estimating the Behavior of Data Rate Adaptation Algorithms in WLAN Hotspots.

Sven Wiethölter and Andreas Ruttor and Uwe Bergemann and Manfred Opper and Adam Wolisz (2013). DARA: Estimating the Behavior of Data Rate Adaptation Algorithms in WLAN Hotspots.

Florian Stimberg and Manfred Opper and Andreas Ruttor (2012). Bayesian Inference for Change Points in Dynamical Systems with Reusable States—a Chinese Restaurant Process Approach. Proceedings of the International Conference on Artificial Intelligence and Statistics

Luís Francisco Seoane Iglesias and Andreas Ruttor (2012). Successful attack on permutation-parity-machine-based neural cryptography. Physical Review E, 025101(R).

Florian Stimberg and Andreas Ruttor and Manfred Opper (2011). Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling. Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB). Tampere University of Technology, Tampere, Finland, 169–172.

Florian Stimberg and Manfred Opper and Guido Sanguinetti and Andreas Ruttor (2011). Inference in continuous-time change-point models. Advances in Neural Information Processing Systems 24, 2717–2725.

Andreas Ruttor and Guido Sanguinetti and Manfred Opper (2010). Approximate inference for stochastic reaction processes. Learning and Inference in Computational Systems Biology. MIT Press, 189-205.

Andreas Ruttor and Manfred Opper (2010). Approximate parameter inference in a stochastic reaction-diffusion model. Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010. JMLR, 669-676.

Manfred Opper and Andreas Ruttor and Guido Sanguinetti (2010). Approximate inference in continuous time Gaussian-Jump processes. Advances in Neural Information Processing Systems 23, 1831–1839.

Andreas Ruttor and Florian Stimberg and Manfred Opper (2010). Comparing diffusion and weak noise approximations for inference in reaction models. Proceedings of the Fourth International Workshop on Machine Learning in Systems Biology (October 15-16, 2010, Edinburgh, UK), 149–152.

Florian Stimberg and Andreas Ruttor and Manfred Opper (2010). MCMC for continuous time switching models. NIPS Workshop on Monte Carlo Methods for Modern Applications (December 10, 2010, Whistler, Canada)

Andreas Ruttor and Guido Sanguinetti and Manfred Opper (2009). Approximate inference for stochastic reaction processes. Learning and Inference in Computational Systems Biology. The MIT Press, 189–205.