Παρακολούθηση
Armin Lederer
Armin Lederer
ETH Zurich
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα inf.ethz.ch - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
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
1822019
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
63*2021
Learning stable Gaussian process state space models
J Umlauft, A Lederer, S Hirche
2017 American Control Conference (ACC), 1499-1504, 2017
472017
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
43*2022
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
362020
Cooperative control of uncertain multiagent systems via distributed Gaussian processes
A Lederer, Z Yang, J Jiao, S Hirche
IEEE Transactions on Automatic Control 68 (5), 3091-3098, 2023
332023
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
312020
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
272022
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
272020
Koopman kernel regression
P Bevanda, M Beier, A Lederer, S Sosnowski, E Hüllermeier, S Hirche
Advances in Neural Information Processing Systems 36, 2024
212024
Posterior variance analysis of Gaussian processes with application to average learning curves
A Lederer, J Umlauft, S Hirche
arXiv preprint arXiv:1906.01404, 2019
212019
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
202021
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
192021
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
172021
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
162020
Parameter Optimization for Learning-based Control of Control-Affine Systems
A Lederer, A Capone, S Hirche
Learning for Dynamics and Control, 465-475, 2020
162020
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
162019
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
15*2023
Learning stable nonparametric dynamical systems with Gaussian process regression
W Xiao, A Lederer, S Hirche
IFAC-PapersOnLine 53 (2), 1194-1199, 2020
82020
Safe reinforcement learning via confidence-based filters
S Curi, A Lederer, S Hirche, A Krause
2022 IEEE 61st Conference on Decision and Control (CDC), 3409-3415, 2022
72022
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