Approximate Inference in Collective Graphical Models
We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical model...
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2013
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sg-smu-ink.sis_research-31992018-06-26T08:40:22Z Approximate Inference in Collective Graphical Models SHELDON, Daniel SUN, Tao KUMAR, Akshat DIETTERICH, Thomas G. We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NP-hard MAP inference problem in CGMs, and show how to use MAP inference for approximate marginal inference within the EM framework. We demonstrate empirically that these approximation techniques can reduce the computational cost of inference by two orders of magnitude and the cost of learning by at least an order of magnitude while providing solutions of equal or better quality. 2013-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2199 https://ink.library.smu.edu.sg/context/sis_research/article/3199/viewcontent/sheldon13.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing |
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Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing SHELDON, Daniel SUN, Tao KUMAR, Akshat DIETTERICH, Thomas G. Approximate Inference in Collective Graphical Models |
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We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NP-hard MAP inference problem in CGMs, and show how to use MAP inference for approximate marginal inference within the EM framework. We demonstrate empirically that these approximation techniques can reduce the computational cost of inference by two orders of magnitude and the cost of learning by at least an order of magnitude while providing solutions of equal or better quality. |
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text |
author |
SHELDON, Daniel SUN, Tao KUMAR, Akshat DIETTERICH, Thomas G. |
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SHELDON, Daniel SUN, Tao KUMAR, Akshat DIETTERICH, Thomas G. |
author_sort |
SHELDON, Daniel |
title |
Approximate Inference in Collective Graphical Models |
title_short |
Approximate Inference in Collective Graphical Models |
title_full |
Approximate Inference in Collective Graphical Models |
title_fullStr |
Approximate Inference in Collective Graphical Models |
title_full_unstemmed |
Approximate Inference in Collective Graphical Models |
title_sort |
approximate inference in collective graphical models |
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Institutional Knowledge at Singapore Management University |
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2013 |
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https://ink.library.smu.edu.sg/sis_research/2199 https://ink.library.smu.edu.sg/context/sis_research/article/3199/viewcontent/sheldon13.pdf |
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