TY - GEN
T1 - Moderated and drifting linear dynamical systems
AU - Guan, Jinyan
AU - Simek, Kyle
AU - Brau, Ernesto
AU - Morrison, Clayton T.
AU - Butler, Emily A.
AU - Barnard, Kobus
N1 - Funding Information:
Research reported in this publication was supported by the National Science Foundation under Award Number BCS-1322940 and the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R21HL109746.
Publisher Copyright:
© Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84970021412
T3 - 32nd International Conference on Machine Learning, ICML 2015
SP - 2463
EP - 2472
BT - 32nd International Conference on Machine Learning, ICML 2015
A2 - Bach, Francis
A2 - Blei, David
PB - International Machine Learning Society (IMLS)
T2 - 32nd International Conference on Machine Learning, ICML 2015
Y2 - 6 July 2015 through 11 July 2015
ER -