The 23rd Statistical Machine Learning Seminar (2015.3.31)

Time: March 31, 2015. 15:00-
Place: Seminar Room 5

Speaker: Gregor Gebhardt (TU Darmstadt, Germany)

Title: The Generalized Kernel Kalman Filter - Learning Forward Models from High Dimensional Observations
Learning forward models from high-dimensional partial observations of the real state is a challenging machine learning problem. Recently, non-parametric inference methods have been pro- posed to tackle such problems. However, such methods either do not provide an uncertainty estimate, are computationally expensive, or can only be applied to a limited set of problems. We generalize the formulation of Kalman Filters (KF) embeddings into a reproducing kernel Hilbert space (RKHS) to be applicable to systems with high-dimensional, partial observations. Our formulation provides probabilistic state estimations and predictions for non-linear dynamical systems that can also be directly learned from the observations. Additionally, we propose an alternative formulation of the RKHS embedding of a conditional density that allows to learn from large data sets, while maintaining computational efficiency. We show on a nonlinear state estimation task with high dimensional observations that our approach provides an improved estimation accuracy.