Lukas P. Fröhlich
Lukas P. Fröhlich
PhD Student, ETH Zurich
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Cited by
Cited by
Meta-learning acquisition functions for transfer learning in bayesian optimization
M Volpp, LP Fröhlich, K Fischer, A Doerr, S Falkner, F Hutter, C Daniel
arXiv preprint arXiv:1904.02642, 2019
On simulation and trajectory prediction with gaussian process dynamics
L Hewing, E Arcari, LP Fröhlich, MN Zeilinger
Learning for Dynamics and Control, 424-434, 2020
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
LP Fröhlich, ED Klenske, J Vinogradska, C Daniel, MN Zeilinger
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
Meta-learning acquisition functions for bayesian optimization
M Volpp, L Fröhlich, A Doerr, F Hutter, C Daniel
arXiv preprint arXiv:1904.02642, 2019
Cautious bayesian optimization for efficient and scalable policy search
LP Fröhlich, MN Zeilinger, ED Klenske
Learning for Dynamics and Control, 227-240, 2021
Bayesian optimization for policy search in high-dimensional systems via automatic domain selection
LP Fröhlich, ED Klenske, CG Daniel, MN Zeilinger
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
On-Policy Model Errors in Reinforcement Learning
LP Fröhlich, M Lefarov, MN Zeilinger, F Berkenkamp
arXiv preprint arXiv:2110.07985, 2021
Kernel Design for Gaussian Mixture Models in Direct Policy Search
LP Fröhlich, L Rozo, MN Zeilinger
Robotics: Science and Systems Conference (RSS) Workshop on Geometry and …, 2021
Method for optimizing a policy for a robot
L Froehlich, E Klenske, L Rozo
US Patent App. 17/450,794, 2022
Data-Efficient Controller Tuning and Reinforcement Learning
L Fröhlich
ETH Zurich, 2022
Controller and method for selecting evaluation points for a bayesian optimization method
E Klenske, L Froehlich
US Patent App. 17/323,785, 2021
Model Learning and Contextual Controller Tuning for Autonomous Racing
LP Fröhlich, C Küttel, E Arcari, L Hewing, MN Zeilinger, A Carron
arXiv preprint arXiv:2110.02710, 2021
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