Moderated and drifting linear dynamical systems

Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton T. Morrison, Emily A. Butler, Kobus Barnard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We consider linear dynamical systems, particularly coupled linear oscillators, where the parameters represent meaningful values in a domain theory, and thus learning what affects them contributes to explanation. Rather than allow perturbations of latent states, we assume that temporal variation beyond noise is explained by parameter drift, and variation across coupled systems is a function of moderating variables. This change in model structure reduces opportunities for efficient inference, and we propose sampling procedures to learn and fit the models. We test our approach on a real dataset of self-recalled emotional experience measurements of heterosexual couples engaged in a conversation about a potentially emotional topic, with body mass index (BMI) being considered as a moderator. We evaluate several models on their ability to predict future conversation dynamics (the last 20% of the data for each test couple), with shared parameters being learned using held out data. We validate the hypothesis that BMI affects the conversation dynamic in the experimentally chosen topic.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Electronic)9781510810587
StatePublished - 2015
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015


Other32nd International Conference on Machine Learning, ICML 2015

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications


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