Probabilistic recurrent state-space models A Doerr, C Daniel, M Schiegg, NT Duy, S Schaal, M Toussaint, ... International conference on machine learning, 1280-1289, 2018 | 143 | 2018 |
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 | 87 | 2019 |
Direct Loss Minimization Inverse Optimal Control. A Doerr, ND Ratliff, J Bohg, M Toussaint, S Schaal Robotics: science and systems, 2015 | 53 | 2015 |
Model-based policy search for automatic tuning of multivariate PID controllers A Doerr, D Nguyen-Tuong, A Marco, S Schaal, S Trimpe 2017 IEEE International Conference on Robotics and Automation (ICRA), 5295-5301, 2017 | 50 | 2017 |
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 | 47 | 2017 |
Learning Gaussian processes by minimizing PAC-Bayesian generalization bounds D Reeb, A Doerr, S Gerwinn, B Rakitsch Advances in Neural Information Processing Systems 31, 2018 | 42 | 2018 |
Trajectory-Based Off-Policy Deep Reinforcement Learning A Doerr, M Volpp, M Toussaint, S Trimpe, C Daniel International Conference on Machine Learning, 2019 | 6 | 2019 |
Monte carlo methods in uncertainity quantification A Dörr, M Mögerle, M Schneider Journal of Statistical Computation and Simulation 58 (2), 99-120, 2014 | 5 | 2014 |
Meta-learning acquisition functions for bayesian optimization M Volpp, LP Fröhlich, A Doerr, F Hutter, C Daniel CoRR, 2019 | 4 | 2019 |
Adaptive and Learning Concepts in Hydraulic Force Control A Doerr Universität Stuttgart Stuttgart, 2015 | | 2015 |
Policy search for imitation learning A Doerr | | 2015 |