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Stochastic Differential Equations with Variational Wishart Diffusions

Jørgensen, M; Deisenroth, MP; Salimbeni, H; (2020) Stochastic Differential Equations with Variational Wishart Diffusions. In: Proceedings of the International Conference on Machine Learning. ICML Proceedings: Online (virtual conference). Green open access

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Abstract

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation, also known as the diffusion, and modelling it by means of Wishart processes. Further, we present a semiparametric approach that allows the framework to scale to high dimensions. This successfully lead us onto how to model both latent and autoregressive temporal systems with conditional heteroskedastic noise. We provide experimental evidence that modelling diffusion often improves performance and that this randomness in the differential equation can be essential to avoid overfitting.

Type: Proceedings paper
Title: Stochastic Differential Equations with Variational Wishart Diffusions
Event: International Conference on Machine Learning (ICML)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://proceedings.icml.cc/static/paper_files/icm...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10105204
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