Abstract Details
Activity Number:
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48
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Type:
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Invited
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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IMS
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Abstract - #307022 |
Title:
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Ensemble Learning for Big Data
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Author(s):
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Hugh A. Chipman*+ and Robert E. McCulloch and Matthew Pratola and Dave Higdon and James Gattiker and Steven L Scott
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Companies:
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Acadia University and The University of Chicago Booth School of Business and Simon Fraser University and Los Alamos National Laboratories and Los Alamos National Laboratories and Google
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Keywords:
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supervised learning ;
Bayesian methods ;
tree model ;
ensemble ;
big data ;
high-performance computing
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Abstract:
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Ensembles are powerful supervised learning methods that combine together many individual models in an adaptive manner. Bayesian Additive Regression Trees (BART) are a successful statistical version of ensemble models, offering full inference. However, computation can be expensive for big data, especially for MCMC. We discuss two different approaches to parallelizing BART, either via MPI or via a novel implementation that runs parallel MCMC runs on independent machines.
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Authors who are presenting talks have a * after their name.
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