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2020 – today
- 2022
[b1]Philippe Wenk:
Learning time-continuous dynamics models with Gaussian-process-based gradient matching. ETH Zurich, Zürich, Switzerland, 2022- [c7]Edoardo Caldarelli
, Philippe Wenk, Stefan Bauer, Andreas Krause:
Adaptive Gaussian Process Change Point Detection. ICML 2022: 2542-2571 - 2021
- [c6]Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler:
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems. NeurIPS 2021: 11870-11882 - [c5]Lenart Treven, Philippe Wenk, Florian Dörfler, Andreas Krause:
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models. NeurIPS 2021: 29780-29793 - [i6]Lenart Treven, Philippe Wenk, Florian Dörfler, Andreas Krause:
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models. CoRR abs/2106.11609 (2021) - [i5]Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler:
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems. CoRR abs/2110.14296 (2021) - 2020
- [c4]Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. AAAI 2020: 6364-6371 - [c3]Diego Agudelo-España, Andrii Zadaianchuk, Philippe Wenk, Aditya Garg, Joel Akpo, Felix Grimminger, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Georg Martius, Andreas Krause, Bernhard Schölkopf, Stefan Bauer, Manuel Wüthrich:
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models. ICRA 2020: 8151-8157 - [i4]Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause:
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives. CoRR abs/2003.02658 (2020)
2010 – 2019
- 2019
- [c2]Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann:
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. AISTATS 2019: 1351-1360 - [c1]Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer:
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. ICML 2019: 1-10 - [i3]Philippe Wenk, Gabriele Abbati, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. CoRR abs/1902.06278 (2019) - [i2]Gabriele Abbati, Philippe Wenk, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs. CoRR abs/1902.08480 (2019) - 2018
- [i1]Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann:
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs. CoRR abs/1804.04378 (2018)
Coauthor Index
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