Michael Volpp

According to our database, Michael Volpp authored at least 11 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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., April, 2023

Latent Task-Specific Graph Network Simulators.

Philipp Dahlinger, Niklas Freymuth, Michael Volpp, Tai Hoang, Gerhard Neumann

CoRR, 2023

Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift.

Florian Seligmann, Philipp Becker, Michael Volpp, Gerhard Neumann

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference.

Michael Volpp, Philipp Dahlinger, Philipp Becker, Christian Daniel, Gerhard Neumann

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models.

Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann

CoRR, 2022

What Matters For Meta-Learning Vision Regression Tasks?

Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Bayesian Context Aggregation for Neural Processes.

Michael Volpp, Fabian Flürenbrock, Lukas Großberger, Christian Daniel, Gerhard Neumann

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Meta-Learning Acquisition Functions for Bayesian Optimization.

Michael Volpp, Lukas P. Fröhlich, Andreas Doerr, Frank Hutter, Christian Daniel

CoRR, 2019

Trajectory-Based Off-Policy Deep Reinforcement Learning.

Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel

Proceedings of the 36th International Conference on Machine Learning, 2019