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Ernesto Brau

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2 papers

2 author rows

AAAI Conference 2016 Conference Paper

Bayesian Inference of Recursive Sequences of Group Activities from Tracks

Ernesto Brau
Colin Dawson
Alfredo Carrillo
David Sidi
Clayton Morrison

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.

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ICML Conference 2015 Conference Paper

Moderated and Drifting Linear Dynamical Systems

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

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 of focus 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 physiological measures 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. As proof of concept, we validate the hypothesis that BMI affects the conversation dynamic in the experimentally chosen topic.

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