Christian Daniel
Christian Daniel
Bosch Center for Artificial Intelligence
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Cited by
Cited by
Probabilistic movement primitives
A Paraschos, C Daniel, JR Peters, G Neumann
Advances in neural information processing systems 26, 2013
Hierarchical Relative Entropy Policy Search
C Daniel, G Neumann, J Peters
Using probabilistic movement primitives in robotics
A Paraschos, C Daniel, J Peters, G Neumann
Autonomous Robots 42 (3), 529-551, 2018
Towards learning hierarchical skills for multi-phase manipulation tasks
O Kroemer, C Daniel, G Neumann, H Van Hoof, J Peters
2015 IEEE international conference on robotics and automation (ICRA), 1503-1510, 2015
Probabilistic inference for determining options in reinforcement learning
C Daniel, H Van Hoof, J Peters, G Neumann
Machine Learning 104 (2), 337-357, 2016
Active Reward Learning.
C Daniel, M Viering, J Metz, O Kroemer, J Peters
Robotics: Science and systems 98, 2014
Probabilistic recurrent state-space models
A Doerr, C Daniel, M Schiegg, D Nguyen-Tuong, S Schaal, M Toussaint, ...
International Conference on Machine Learning (ICML), 2018
Learning step size controllers for robust neural network training
C Daniel, J Taylor, S Nowozin
Thirtieth AAAI Conference on Artificial Intelligence, 2016
Learning concurrent motor skills in versatile solution spaces
C Daniel, G Neumann, J Peters
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2012
Learning sequential motor tasks
C Daniel, G Neumann, O Kroemer, J Peters
2013 IEEE International Conference on Robotics and Automation, 2626-2632, 2013
Active reward learning with a novel acquisition function
C Daniel, O Kroemer, M Viering, J Metz, J Peters
Autonomous Robots 39 (3), 389-405, 2015
Optimizing long-term predictions for model-based policy search
A Doerr, C Daniel, D Nguyen-Tuong, A Marco, S Schaal, T Marc, S Trimpe
Conference on Robot Learning, 227-238, 2017
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
Learning modular policies for robotics
G Neumann, C Daniel, A Paraschos, A Kupcsik, J Peters
Frontiers in computational neuroscience 8, 62, 2014
Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems
H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig
International Conference on Machine Learning, 5198-5208, 2020
Noisy-input entropy search for efficient robust bayesian optimization
L Fröhlich, E Klenske, J Vinogradska, C Daniel, M Zeilinger
International Conference on Artificial Intelligence and Statistics, 2262-2272, 2020
Reinforcement learning vs human programming in tetherball robot games
S Parisi, H Abdulsamad, A Paraschos, C Daniel, J Peters
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
Iterative sle solvers over a cpu-gpu platform
APD Binotto, C Daniel, D Weber, A Kuijper, A Stork, C Pereira, D Fellner
2010 IEEE 12th International Conference on High Performance Computing and …, 2010
Information-theoretic motor skill learning
G Neumann, C Daniel, A Kupcsik, M Deisenroth, J Peters
Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013
Meta-learning acquisition functions for bayesian optimization
M Volpp, L Fröhlich, A Doerr, F Hutter, C Daniel
arXiv preprint arXiv:1904.02642, 2019
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