ARES and MarS - adversarial and MMD-minimizing regression for SDEs

Conference item

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we avoid the discretization schemes of classical approaches. This leads to significant improvements in parameter accuracy and robustness given random initial guesses. On four established benchmark systems, we compare the performance of our algorithms to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.

Authors: Abbati, G; Wenk, P; Osborne, MA; Krause, A; Schölkopf, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: PMLR
• Host title: Proceedings of the 36th International Conference on Machine Learning
• Volume: 97
• Pages: 1-10
• Series: Proceedings of Machine Learning Research
• Publication date: 2019-06-16
• Event title: 36th International Conference on Machine Learning (ICML 2019)
• Event location: Long Beach, CA, USA
• Event website: https://icml.cc/Conferences/2019
• Event start date: 2019-06-09
• Event end date: 2019-06-15
• ISSN: 2640-3498
• Language: English