16 citations · 73 across the 34 of their papers we have counts for
8 papers · 1 filter
The Impact of Data on the Stability of Learning-Based Control- Extended Version
Armin Lederer, Alexandre Capone, Thomas Beckers +2
Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, w…
Deep Learning based Uncertainty Decomposition for Real-time Control
Neha Das, Jonas Umlauft, Armin Lederer +2
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty…
GP3: A Sampling-based Analysis Framework for Gaussian Processes
Armin Lederer, Markus Kessler, Sandra Hirche
Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approa…
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Wenxin Xiao, Armin Lederer, Sandra Hirche
Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because label…
Localized active learning of Gaussian process state space models
Alexandre Capone, Jonas Umlauft, Thomas Beckers +2
The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accura…
Real-Time Regression with Dividing Local Gaussian Processes
Armin Lederer, Alejandro Jose Ordonez Conejo, Korbinian Maier +3
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models. While exact Gaussian process regres…