Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Proceedings of Machine Learning Research, 48, pp. 1699-1707.
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Publisher's URL: http://proceedings.mlr.press/v48/niu16.html
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: | Filippone, Dr Maurizio and Husmeier, Professor Dirk and Niu, Dr Mu and Rogers, Dr Simon |
| 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: | Proceedings of Machine Learning Research |
| Publisher: | PMLR |
| ISSN: | 1938-7228 |
| ISSN (Online): | 1533-7928 |
| Copyright Holders: | Copyright © 2016 The Authors |
| First Published: | First published in Proceedings of Machine Learning Research 48: 1699-1707 |
| Publisher Policy: | Reproduced with the permission of the authors |
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