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ORCIDMichael Volpp
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2020 – today
- 2023
[j2]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): 2325-2332 (2023)- [j1]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) - [c6]Michael Volpp, Philipp Dahlinger, Philipp Becker, Christian Daniel, Gerhard Neumann:
Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. ICLR 2023 - [c5]Florian Seligmann, Philipp Becker, Michael Volpp, Gerhard Neumann:
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. NeurIPS 2023 - [i7]Florian Seligmann, Philipp Becker, Michael Volpp, Gerhard Neumann:
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. CoRR abs/2306.12306 (2023) - [i6]Philipp Dahlinger, Niklas Freymuth, Michael Volpp, Tai Hoang, Gerhard Neumann:
Latent Task-Specific Graph Network Simulators. CoRR abs/2311.05256 (2023) - 2022
- [c4]Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann:
What Matters For Meta-Learning Vision Regression Tasks? CVPR 2022: 14756-14766 - [i5]Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann:
What Matters For Meta-Learning Vision Regression Tasks? CoRR abs/2203.04905 (2022) - [i4]Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann:
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models. CoRR abs/2209.11533 (2022) - [i3]Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann:
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives. CoRR abs/2210.01531 (2022) - 2021
- [c3]Michael Volpp, Fabian Flürenbrock, Lukas Großberger, Christian Daniel, Gerhard Neumann:
Bayesian Context Aggregation for Neural Processes. ICLR 2021 - 2020
- [c2]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
2010 – 2019
- 2019
- [c1]Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel:
Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML 2019: 1636-1645 - [i2]Michael Volpp, Lukas P. Fröhlich, Andreas Doerr, Frank Hutter, Christian Daniel:
Meta-Learning Acquisition Functions for Bayesian Optimization. CoRR abs/1904.02642 (2019) - [i1]Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel:
Trajectory-Based Off-Policy Deep Reinforcement Learning. CoRR abs/1905.05710 (2019)
Coauthor Index
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