9 citations · 14 across the 18 of their papers we have counts for
19 papers
Consensus-based optimization for linearly separable functions
Christian Fiedler, Tim Roith
Consensus-based optimization (CBO) is an efficient metaheuristic for global optimisation with attractive mathematical properties, allowing global convergence results even in non-co…
Reliable sampling-based RKHS norm estimation via superconvergence
Tizian Wenzel, Abdullah Tokmak, Christian Fiedler
Kernel methods are one of the cornerstones of learning-based control, modern system identification, surrogate modelling, and related fields. A key advantage of this class of learni…
The Mean-Field Limit of Online Stochastic Vector Balancing
Christian Fiedler, Joe Jackson, Daniel Lacker +1
We study an online vector balancing problem, in which independent Gaussian random vectors , each of dimension $n…
Statistical Learning Theory for Distributional Classification
Christian Fiedler
In supervised learning with distributional inputs in the two-stage sampling setup, relevant to applications like learning-based medical screening or causal learning, the inputs (wh…
Fuk-Nagaev inequality in smooth Banach spaces: Optimum bounds for distributions of heavy-tailed martingales
Mattes Mollenhauer, Christian Fiedler
We derive a Fuk-Nagaev inequality for the maxima of norms of martingale sequences in smooth Banach spaces which allow for a finite number of higher conditional moments. The bound i…
Towards optimal control of ensembles of discrete-time systems
Christian Fiedler, Alessandro Scagliotti
The control of ensembles of dynamical systems is an intriguing and challenging problem, arising for example in quantum control. We initiate the investigation of optimal control of…
Kernel conditional tests from learning-theoretic bounds
Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck +2
We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct statistical tests of functionals of conditional distributions…
Safety in safe Bayesian optimization and its ramifications for control
Christian Fiedler, Johanna Menn, Sebastian Trimpe
A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only throu…
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…
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…
Revisiting the derivation of stage costs in infinite horizon discrete-time optimal control
Christian Fiedler, Sebastian Trimpe
In many applications of optimal control, the stage cost is not fixed, but rather a design choice with considerable impact on the control performance. In infinite horizon optimal co…
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many rea…
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estima…
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
Christian Fiedler, Massimo Fornasier, Timo Klock +1
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 fi…
A Kernel Two-sample Test for Dynamical Systems
Friedrich Solowjow, Dominik Baumann, Christian Fiedler +3
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems. This is particularly relevant for data generated by dynam…