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

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

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)

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

What Matters For Meta-Learning Vision Regression Tasks? - Ning Gao; Hanna Ziesche; Ngo Anh Vien; Michael Volpp; Gerhard Neumann (CVPR, 2022)

ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives. - Ge Li; Zeqi Jin; Michael Volpp; Fabian Otto; Rudolf Lioutikov; Gerhard Neumann (CoRR, 2022)

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

Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. - Florian Seligmann; Philipp Becker; Michael Volpp; Gerhard Neumann (NeurIPS, 2023)

Latent Task-Specific Graph Network Simulators. - Philipp Dahlinger; Niklas Freymuth; Michael Volpp; Tai Hoang; Gerhard Neumann (CoRR, 2023)

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. - Oleg Arenz; Philipp Dahlinger; Zihan Ye; Michael Volpp; Gerhard Neumann (Trans. Mach. Learn. Res., 2023)

ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives. - Ge Li; Zeqi Jin; Michael Volpp; Fabian Otto; Rudolf Lioutikov; Gerhard Neumann (IEEE Robotics Autom. Lett., 2023)