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.
119575.pdf - Accepted Version
Publisher's URL: http://jmlr.org/proceedings/papers/v48/niu16.html
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.
|Additional Information:||Proceedings of The 33rd International Conference on Machine Learning, New York, NY, 19-24 June 2016.|
|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|
|Copyright Holders:||Copyright © 2016 The Authors|
|Publisher Policy:||Reproduced with the permission of the authors|