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Armin Lederer
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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
1492019
Gaussian process-based real-time learning for safety critical applications
A Lederer, AJO Conejo, KA Maier, W Xiao, J Umlauft, S Hirche
International Conference on Machine Learning, 6055-6064, 2021
45*2021
Learning stable Gaussian process state space models
J Umlauft, A Lederer, S Hirche
2017 American Control Conference (ACC), 1499-1504, 2017
422017
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
312020
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
252020
Diffeomorphically learning stable Koopman operators
P Bevanda, M Beier, S Kerz, A Lederer, S Sosnowski, S Hirche
IEEE Control Systems Letters 6, 3427-3432, 2022
22*2022
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
222020
Gaussian process uniform error bounds with unknown hyperparameters for safety-critical applications
A Capone, A Lederer, S Hirche
International Conference on Machine Learning, 2609-2624, 2022
182022
Posterior variance analysis of Gaussian processes with application to average learning curves
A Lederer, J Umlauft, S Hirche
arXiv preprint arXiv:1906.01404, 2019
182019
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
162021
Parameter Optimization for Learning-based Control of Control-Affine Systems
A Lederer, A Capone, S Hirche
Learning for Dynamics and Control, 465-475, 2020
152020
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
142021
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
2021 60th IEEE Conference on Decision and Control (CDC), 4406-4411, 2021
132021
Can learning deteriorate control? analyzing computational delays in gaussian process-based event-triggered online learning
X Dai, A Lederer, Z Yang, S Hirche
Learning for Dynamics and Control Conference, 445-457, 2023
12*2023
Cooperative control of uncertain multi-agent systems via distributed gaussian processes
A Lederer, Z Yang, J Jiao, S Hirche
IEEE Transactions on Automatic Control, 2022
122022
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
122020
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
122019
Koopman kernel regression
P Bevanda, M Beier, A Lederer, S Sosnowski, E Hüllermeier, S Hirche
Advances in Neural Information Processing Systems 36, 2024
72024
Learning stable nonparametric dynamical systems with Gaussian process regression
W Xiao, A Lederer, S Hirche
IFAC-PapersOnLine 53 (2), 1194-1199, 2020
72020
Deep Learning based Uncertainty Decomposition for Real-time Control
N Das, J Umlauft, A Lederer, A Capone, T Beckers, S Hirche
IFAC-PapersOnLine 56 (2), 847-853, 2023
5*2023
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