Philippe Wenk

2020
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems P. Wenk, G. Abbati, S. Bauer, M. A. Osborne, A. Krause, B. Schölkopf In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020 [bibtex] [abstract] [pdf]
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives E. Angelis, P. Wenk, B. Schölkopf, S. Bauer, A. Krause In arXiv preprint arXiv:2003.02658, 2020 [bibtex] [abstract]
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models D. Agudelo-España, A. Zadaianchuk, P. Wenk, A. Garg, J. Akpo, F. Grimminger, J. Viereck, M. Naveau, L. Righetti, G. Martius, A. Krause, B. Schölkopf, S. Bauer, M. Wüthrich In 2020 IEEE International Conference on Robotics and Automation (ICRA), volume , 2020 [bibtex] [abstract] [doi]
2019
AReS and MaRS – Adversarial and MMD-Minimizing Regression for SDEs G. Abbati, P. Wenk, S. Bauer, M. A. Osborne, A. Krause, B. Schölkopf In Proc. International Conference on Machine Learning (ICML), 2019 Oral presentation [bibtex] [abstract] [pdf]
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs P. Wenk, A. Gotovos, S. Bauer, N. S. Gorbach, A. Krause, J. M. Buhmann In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 [bibtex] [abstract] [pdf]