Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

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Abstract

We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distributions that match observations with non-uniform time increments and arbitrary sparseness, which is in contrast with gradient matching that does not optimize simulated responses. We formulate sensitivity equations for learning and demonstrate that our general stochastic distribution optimisation leads to robust and efficient learning of SDE systems.

Details

Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018
Conference number: 28

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryDenmark
CityAalborg
Period17/09/201820/09/2018

    Research areas

  • Stochastic differential equations, Gaussian processes

ID: 28485668