Fast inference in nonlinear dynamical systems using gradient matching

Niu, M., Rogers, S., Filippone, M., and Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Journal of Machine Learning Research: Workshop and Conference Proceedings, 48, pp. 1699-1707.

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Abstract

Parameter inference in mechanistic models of coupled differential equations is a topical problem. We propose a new method based on kernel ridge regression and gradient matching, and an objective function that simultaneously encourages goodness of fit and penalises inconsistencies with the differential equations. Fast minimisation is achieved by exploiting partial convexity inherent in this function, and setting up an iterative algorithm in the vein of the EM algorithm. An evaluation of the proposed method on various benchmark data suggests that it compares favourably with state-of-the-art alternatives.

Item Type:Articles
Additional Information:Proceedings of The 33rd International Conference on Machine Learning, New York, NY, 19-24 June 2016.
Status:Published
Refereed:No
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Rogers, Dr Simon and Filippone, Dr Maurizio and Niu, Dr Mu
Authors: Niu, M., Rogers, S., Filippone, M., and Husmeier, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Machine Learning Research: Workshop and Conference Proceedings
Publisher:Journal of Machine Learning Research
ISSN:1938-7228
ISSN (Online):1533-7928
Copyright Holders:Copyright © 2016 The Authors
Publisher Policy:Reproduced with the permission of the authors

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering & Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS