@inproceedings{9a14f8a875ba45f19ccb971e9043623c,
title = "Fast inference in nonlinear dynamical systems using gradient matching",
abstract = "Parameter inference in mechanistic models of coupled differential equations is a topical prob-lem. 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.",
author = "Mu Niu and Simon Rogers and Maurizio Filippone and Dirk Husmeier",
note = "Publisher Copyright: {\textcopyright} 2016 by the author(s).; 33rd International Conference on Machine Learning, ICML 2016 ; Conference date: 19-06-2016 Through 24-06-2016",
year = "2016",
language = "English (US)",
series = "33rd International Conference on Machine Learning, ICML 2016",
publisher = "International Machine Learning Society (IMLS)",
pages = "2555--2563",
editor = "Weinberger, {Kilian Q.} and Balcan, {Maria Florina}",
booktitle = "33rd International Conference on Machine Learning, ICML 2016",
}