no code implementations • 19 Mar 2024 • Christian Fiedler, Johanna Menn, Lukas Kreisköther, Sebastian Trimpe
To overcome this challenge, we introduce the Lipschitz-only Safe Bayesian Optimization (LoSBO) algorithm, which guarantees safety without an assumption on the RKHS bound, and empirically show that this algorithm is not only safe, but also exhibits superior performance compared to the state-of-the-art on several function classes.
1 code implementation • 15 Dec 2023 • Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger, Sebastian Trimpe, Johannes Köhler
We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees.
no code implementations • 11 Dec 2023 • 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.
no code implementations • 27 Oct 2023 • 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.
no code implementations • 27 Oct 2023 • Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered.
no code implementations • 28 Feb 2023 • Christian Fiedler, Michael Herty, Michael Rom, Chiara Segala, Sebastian Trimpe
Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular and successful machine learning techniques.
no code implementations • 7 May 2021 • Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
The combination of machine learning with control offers many opportunities, in particular for robust control.
1 code implementation • 6 May 2021 • Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required.
no code implementations • 18 Jan 2021 • Christian Fiedler, Massimo Fornasier, Timo Klock, Michael Rauchensteiner
In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a finite number of input-output samples.
no code implementations • 23 Apr 2020 • Friedrich Solowjow, Dominik Baumann, Christian Fiedler, Andreas Jocham, Thomas Seel, Sebastian Trimpe
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.