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Philippe Wenk
Philippe Wenk
ETH Zürich, MPI Tübingen
Verified email at ethz.ch
Title
Cited by
Cited by
Year
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs
P Wenk, A Gotovos, S Bauer, NS Gorbach, A Krause, JM Buhmann
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
432019
Odin: Ode-informed regression for parameter and state inference in time-continuous dynamical systems
P Wenk, G Abbati, MA Osborne, B Schölkopf, A Krause, S Bauer
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6364-6371, 2020
302020
Learning stable deep dynamics models for partially observed or delayed dynamical systems
A Schlaginhaufen, P Wenk, A Krause, F Dorfler
Advances in Neural Information Processing Systems 34, 11870-11882, 2021
142021
Ares and mars adversarial and mmd-minimizing regression for sdes
G Abbati, P Wenk, MA Osborne, A Krause, B Schölkopf, S Bauer
International Conference on Machine Learning, 1-10, 2019
142019
Adaptive gaussian process change point detection
E Caldarelli, P Wenk, S Bauer, A Krause
International Conference on Machine Learning, 2542-2571, 2022
72022
A real-robot dataset for assessing transferability of learned dynamics models
D Agudelo-Espana, A Zadaianchuk, P Wenk, A Garg, J Akpo, ...
2020 IEEE International Conference on Robotics and Automation (ICRA), 8151-8157, 2020
62020
Sleipnir: Deterministic and provably accurate feature expansion for gaussian process regression with derivatives
E Angelis, P Wenk, B Schölkopf, S Bauer, A Krause
arXiv preprint arXiv:2003.02658, 2020
62020
Distributional gradient matching for learning uncertain neural dynamics models
L Treven, P Wenk, F Dorfler, A Krause
Advances in Neural Information Processing Systems 34, 29780-29793, 2021
22021
Learning time-continuous dynamics models with Gaussian-process-based gradient matching
P Wenk
ETH Zürich Zürich, 2022
2022
Stabilizing Flight
S Gyger, A Steger, P Wenk
2013
Deep Dynamics Learning For Thermoacoustics
P Wenk
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