| Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization M Volpp, LP Fröhlich, K Fischer, A Doerr, S Falkner, F Hutter, C Daniel ICLR 2020, 2019 | 56* | 2019 |
| Bayesian Context Aggregation for Neural Processes M Volpp, F Flürenbrock, L Grossberger, C Daniel, G Neumann ICLR 2021, 2021 | 18 | 2021 |
| What Matters For Meta-Learning Vision Regression Tasks? N Gao, H Ziesche, NA Vien, M Volpp, G Neumann Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 11 | 2022 |
| Trajectory-Based Off-Policy Deep Reinforcement Learning A Doerr, M Volpp, M Toussaint, S Trimpe, C Daniel ICML 2019, 2019 | 5 | 2019 |
| Factorization with a logarithmic energy spectrum of a two-dimensional potential F Gleisberg, M Volpp, WP Schleich Physics Letters A 379 (40-41), 2556-2560, 2015 | 4 | 2015 |
| Standard development process for physical models used in real time applications based on the example of an exhaust pipe model A Gallet, M Volpp, W Lengerer Technical report, Robert Bosch GmbH, 2014 | 4 | 2014 |
| ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives G Li, Z Jin, M Volpp, F Otto, R Lioutikov, G Neumann IEEE Robotics and Automation Letters 8 (4), 2325-2332, 2023 | 3 | 2023 |
| Stable Optimization of Gaussian Likelihoods D Megerle, F Otto, M Volpp, G Neumann | | 2023 |
| Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference M Volpp, P Dahlinger, P Becker, C Daniel, G Neumann ICLR 2023, 2023 | | 2023 |
| A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models O Arenz, P Dahlinger, Z Ye, M Volpp, G Neumann arXiv preprint arXiv:2209.11533, 2022 | | 2022 |
| Configuring a system which interacts with an environment A Doerr, C Daniel, M Volpp US Patent 11,402,808, 2022 | | 2022 |
| Method for ascertaining an output signal with the aid of a machine learning system G Neumann, M Volpp US Patent App. 17/449,139, 2022 | | 2022 |
| Method and device for training a machine learning system G Neumann, M Volpp US Patent App. 17/449,517, 2022 | | 2022 |
| Bayesian context aggregation for neural processes G Neumann, M Volpp US Patent App. 17/446,676, 2022 | | 2022 |
| Running of Radiative Neutrino Masses - A Study of the Zee-Babu Model M Volpp Max Planck Institute for Physics Munich, 2017 | | 2017 |
| Supplementary Material for: What Matters For Meta-Learning Vision Regression Tasks? N Gao, H Ziesche, NA Vien, M Volpp, G Neumann | | |
Christian DanielBosch Center for Artificial IntelligenceVerified email at ias.tu-darmstadt.de
