Armin Lederer
Title
Cited by
Cited by
Year
Uniform error bounds for Gaussian process regression with application to safe control
A Lederer, J Umlauft, S Hirche
Advances in Neural Information Processing Systems 32, 659-669, 2019
362019
Learning stable Gaussian process state space models
J Umlauft, A Lederer, S Hirche
2017 American Control Conference (ACC), 1499-1504, 2017
182017
Localized active learning of Gaussian process state space models
A Capone, G Noske, J Umlauft, T Beckers, A Lederer, S Hirche
Learning for Dynamics and Control, 490-499, 2020
92020
Posterior variance analysis of Gaussian processes with application to average learning curves
A Lederer, J Umlauft, S Hirche
arXiv preprint arXiv:1906.01404, 2019
92019
How training data impacts performance in learning-based control
A Lederer, A Capone, J Umlauft, S Hirche
IEEE Control Systems Letters 5 (3), 905-910, 2020
52020
Smart Forgetting for Safe Online Learning with Gaussian Processes
J Umlauft, T Beckers, A Capone, A Lederer, S Hirche
Learning for Dynamics and Control, 160-169, 2020
52020
Local Asymptotic Stability Analysis and Region of Attraction Estimation with Gaussian Processes
A Lederer, S Hirche
2019 IEEE 58th Conference on Decision and Control (CDC), 1766-1771, 2019
52019
Real-time regression with dividing local gaussian processes
A Lederer, AJO Conejo, K Maier, W Xiao, S Hirche
arXiv preprint arXiv:2006.09446, 2020
42020
Data selection for multi-task learning under dynamic constraints
A Capone, A Lederer, J Umlauft, S Hirche
IEEE Control Systems Letters 5 (3), 959-964, 2020
32020
Parameter Optimization for Learning-based Control of Control-Affine Systems
A Lederer, A Capone, S Hirche
Learning for Dynamics and Control, 465-475, 2020
32020
Distributed learning consensus control for unknown nonlinear multi-agent systems based on gaussian processes
Z Yang, S Sosnowski, Q Liu, J Jiao, A Lederer, S Hirche
arXiv preprint arXiv:2103.15929, 2021
12021
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
A Lederer, J Umlauft, S Hirche
arXiv preprint arXiv:2101.05328, 2021
12021
Real-time Uncertainty Decomposition for Online Learning Control
J Umlauft, A Lederer, T Beckers, S Hirche
arXiv preprint arXiv:2010.02613, 2020
12020
Confidence Regions for Simulations with Learned Probabilistic Models*
A Lederer, Q Hao, S Hirche
2020 American Control Conference (ACC), 3947-3952, 2020
12020
Learning stable nonparametric dynamical systems with Gaussian process regression
W Xiao, A Lederer, S Hirche
arXiv preprint arXiv:2006.07868, 2020
12020
GP3: A Sampling-based Analysis Framework for Gaussian Processes
A Lederer, M Kessler, S Hirche
arXiv preprint arXiv:2006.07871, 2020
12020
The Impact of Data on the Stability of Learning-Based Control
A Lederer, A Capone, T Beckers, J Umlauft, S Hirche
Learning for Dynamics and Control, 623-635, 2021
2021
Inverse Reinforcement Learning a Control Lyapunov Approach
S Tesfazgi, A Lederer, S Hirche
arXiv preprint arXiv:2104.04483, 2021
2021
Distributed Bayesian Online Learning for Cooperative Manipulation
A Lederer, M Dißemond, S Hirche
arXiv preprint arXiv:2104.04342, 2021
2021
Distributed Bayesian Online Learning for Cooperative Manipulation
P Budde gen Dohmann, A Lederer, M Dißemond, S Hirche
arXiv e-prints, arXiv: 2104.04342, 2021
2021
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