Iterative SLE Solvers over a CPU-GPU Platform. - Alécio Pedro Delazari Binotto; Christian Daniel; Daniel Weber 001; Arjan Kuijper; André Stork; Carlos Eduardo Pereira; Dieter W. Fellner (HPCC, 2010)

Learning concurrent motor skills in versatile solution spaces. - Christian Daniel; Gerhard Neumann; Jan Peters 001 (IROS, 2012)

Hierarchical Relative Entropy Policy Search. - Christian Daniel; Gerhard Neumann; Jan Peters 001 (AISTATS, 2012)

Learning sequential motor tasks. - Christian Daniel; Gerhard Neumann; Oliver Kroemer; Jan Peters 001 (ICRA, 2013)

Autonomous reinforcement learning with hierarchical REPS. - Christian Daniel; Gerhard Neumann; Jan Peters 001 (IJCNN, 2013)

Probabilistic Movement Primitives. - Alexandros Paraschos; Christian Daniel; Jan Peters 001; Gerhard Neumann (NIPS, 2013)

Active Reward Learning. - Christian Daniel; Malte Viering; Jan Metz; Oliver Kroemer; Jan Peters 001 (Robotics: Science and Systems, 2014)

Learning modular policies for robotics. - Gerhard Neumann; Christian Daniel; Alexandros Paraschos; Andras Gabor Kupcsik; Jan Peters 001 (Frontiers Comput. Neurosci., 2014)

Towards learning hierarchical skills for multi-phase manipulation tasks. - Oliver Kroemer; Christian Daniel; Gerhard Neumann; Herke van Hoof; Jan Peters 001 (ICRA, 2015)

Reinforcement learning vs human programming in tetherball robot games. - Simone Parisi; Hany Abdulsamad; Alexandros Paraschos; Christian Daniel; Jan Peters 001 (IROS, 2015)

Active reward learning with a novel acquisition function. - Christian Daniel; Oliver Kroemer; Malte Viering; Jan Metz; Jan Peters 001 (Auton. Robots, 2015)

Learning Step Size Controllers for Robust Neural Network Training. - Christian Daniel; Jonathan Taylor; Sebastian Nowozin (AAAI, 2016)

Real-time extension of ROS based on a network of modular blocks for highly precise motion generation. - Christian Scheifele; Armin Lechler; Christian Daniel; Weiliang Xu 001 (AMC, 2016)

Hierarchical Relative Entropy Policy Search. - Christian Daniel; Gerhard Neumann; Oliver Kroemer; Jan Peters 001 (J. Mach. Learn. Res., 2016)

Probabilistic inference for determining options in reinforcement learning. - Christian Daniel; Herke van Hoof; Jan Peters 001; Gerhard Neumann (Mach. Learn., 2016)

Learning hierarchical policies from human ratings. - Christian Daniel (2016)

Optimizing Long-term Predictions for Model-based Policy Search. - Andreas Doerr; Christian Daniel; Duy Nguyen-Tuong; Alonso Marco; Stefan Schaal; Marc Toussaint; Sebastian Trimpe (CoRL, 2017)

Probabilistic Recurrent State-Space Models. - Andreas Doerr; Christian Daniel; Martin Schiegg; Duy Nguyen-Tuong; Stefan Schaal; Marc Toussaint; Sebastian Trimpe (ICML, 2018)

Using probabilistic movement primitives in robotics. - Alexandros Paraschos; Christian Daniel; Jan Peters 001; Gerhard Neumann (Auton. Robots, 2018)

Trajectory-Based Off-Policy Deep Reinforcement Learning. - Andreas Doerr; Michael Volpp; Marc Toussaint; Sebastian Trimpe; Christian Daniel (ICML, 2019)

Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection. - Lukas P. Fröhlich; Edgar D. Klenske; Christian G. Daniel; Melanie N. Zeilinger (IROS, 2019)

Meta-Learning Acquisition Functions for Bayesian Optimization. - Michael Volpp; Lukas P. Fröhlich; Andreas Doerr; Frank Hutter; Christian Daniel (CoRR, 2019)

Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. - Lukas P. Fröhlich; Edgar D. Klenske; Julia Vinogradska; Christian Daniel; Melanie N. Zeilinger (AISTATS, 2020)

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. - Michael Volpp; Lukas P. Fröhlich; Kirsten Fischer; Andreas Doerr; Stefan Falkner; Frank Hutter; Christian Daniel (ICLR, 2020)

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. - Hans Kersting; Nicholas Krämer; Martin Schiegg; Christian Daniel; Michael Tiemann 001; Philipp Hennig (ICML, 2020)

Bayesian Context Aggregation for Neural Processes. - Michael Volpp; Fabian Flürenbrock; Lukas Großberger; Christian Daniel; Gerhard Neumann (ICLR, 2021)

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems. - Joel Oren; Chana Ross; Maksym Lefarov; Felix Richter 001; Ayal Taitler; Zohar Feldman; Dotan Di Castro; Christian Daniel (SOCS, 2021)

Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. - Michael Volpp; Philipp Dahlinger; Philipp Becker; Christian Daniel; Gerhard Neumann (ICLR, 2023)