9 citations · 14 across the 18 of their papers we have counts for
5 papers · 1 filter
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Christian Fiedler, Michael Herty, Sebastian Trimpe
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continu…
Automatic nonlinear MPC approximation with closed-loop guarantees
Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger +2
Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems…
Lipschitz and Hölder Continuity in Reproducing Kernel Hilbert Spaces
Christian Fiedler
Reproducing kernel Hilbert spaces (RKHSs) are very important function spaces, playing an important role in machine learning, statistics, numerical analysis and pure mathematics. Si…
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…
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…