Michael Volpp

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 15

Top Topics

Top Venues

IEEE Robotics Autom. Lett.

Publications

Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives. IEEE Robotics Autom. Lett. 8 (4) (2023)
Florian Seligmann, Philipp Becker, Michael Volpp, Gerhard Neumann
Beyond Deep Ensembles - A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. CoRR (2023)
Florian Seligmann, Philipp Becker, Michael Volpp, Gerhard Neumann
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. NeurIPS (2023)
Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. Trans. Mach. Learn. Res. 2023 (2023)
Michael Volpp, Philipp Dahlinger, Philipp Becker, Christian Daniel, Gerhard Neumann
Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. ICLR (2023)
Philipp Dahlinger, Niklas Freymuth, Michael Volpp, Tai Hoang, Gerhard Neumann
Latent Task-Specific Graph Network Simulators. CoRR (2023)
Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. CoRR (2022)
Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann
What Matters For Meta-Learning Vision Regression Tasks? CoRR (2022)
Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives. CoRR (2022)
Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann
What Matters For Meta-Learning Vision Regression Tasks? CVPR (2022)
Michael Volpp, Fabian Flürenbrock, Lukas Großberger, Christian Daniel, Gerhard Neumann
Bayesian Context Aggregation for Neural Processes. ICLR (2021)
Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. ICLR (2020)
Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel
Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML (2019)
Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel
Trajectory-Based Off-Policy Deep Reinforcement Learning. CoRR (2019)
Michael Volpp, Lukas P. Fröhlich, Andreas Doerr, Frank Hutter, Christian Daniel
Meta-Learning Acquisition Functions for Bayesian Optimization. CoRR (2019)