3 citations · 6 across the 8 of their papers we have counts for
8 papers
Feedforward Controllers from Learned Dynamic Local Model Networks with Application to Excavator Assistance Functions
Leon Greiser, Ozan Demir, Benjamin Hartmann +2
Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in…
On Safety in Safe Bayesian Optimization
Christian Fiedler, Johanna Menn, Lukas Kreisköther +1
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimizati…
On kernel-based statistical learning in the mean field limit
Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when…
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches requ…
Reproducing kernel Hilbert spaces in the mean field limit
Christian Fiedler, Michael Herty, Michael Rom +2
Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular and successful machine learning techniques. From a mathe…
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2
Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…