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Dr. Andreas Ruttor
- © Copyright??
- © Copyright??
Scientific Assistant
Office: MAR 4.019
Phone: +49 30 314-23938
E-Mail: andreas.ruttor <AT> tu-berlin.de
Consulting hour according to agreement.
| Period | Occupation |
|---|---|
| since 2007 | Post-Doc Researcher at TU Berlin, Artificial Intelligence Group |
| 2007 | Ph. D. in Physics, Universität Würzburg |
| 2003-2007 | Research Assistant at Universität Würzburg, Statistical Physics Group |
| 2003 | Master in Physics, Universität Würzburg |
Research Fields
- Stochastic dynamical systems (exact and approximate inference, model selection)
- Statistical learning theory (Gaussian processes, neural networks)
- Statistical physics of complex systems
- Applications: Systems biology, data analysis, cryptography
Publications
| Citation key | BaRuOp16 |
|---|---|
| Author | Philipp Batz and Andreas Ruttor and Manfred Opper |
| Pages | 083404 |
| Year | 2016 |
| Journal | Journal of Statistical Mechanics: Theory and Experiment |
| Volume | 2016 |
| Number | 8 |
| Abstract | We present a method for the nonparametric estimation of the drift function of certain types of stochastic differential equations from the empirical density. It is based on a variational formulation of the Fokker–Planck equation. The minimization of an empirical estimate of the variational functional using kernel based regularization can be performed in closed form. We demonstrate the performance of the method on second order, Langevin-type equations and show how the method can be generalized to other noise models. |
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