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Found 10 papers, 2 papers with code
On Safety in Safe Bayesian Optimization
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.
Automatic nonlinear MPC approximation with closed-loop guarantees
We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees.
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems.
Lipschitz and Hölder Continuity in Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces (RKHSs) are very important function spaces, playing an important role in machine learning, statistics, numerical analysis and pure mathematics.
On kernel-based statistical learning in the mean field limit
In many applications of machine learning, a large number of variables are considered.
Reproducing kernel Hilbert spaces in the mean field limit
Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular and successful machine learning techniques.
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
The combination of machine learning with control offers many opportunities, in particular for robust control.
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
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.
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
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.
A Kernel Two-sample Test for Dynamical Systems
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.
