AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer

Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 101

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 commonly used benchmark systems, we demonstrate the performance of our algorithms compared to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.

Author Information

Gabriele Abbati (University of Oxford)
Philippe Wenk (ETH Zurich)
Michael A Osborne (U Oxford)
Andreas Krause (ETH Zurich)
Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Stefan Bauer (MPI for Intelligent Systems)

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