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University of Massachusetts


Message Passing For Collective Graphical Models

Collective graphical models (CGMs) are a formalism for inference and learning with (noisy) aggregate data that are motivated by a model for bird migration. We discuss the problem of approximate MAP inference in CGMs. We first describe how MAP inference can be formulated as a convex optimization problem using two approximations, and then derive a novel Belief Propagation (BP)-style algorithm for this convex optimization problem. The algorithm is a strict generalization of BP. We show using synthetic datasets that this combination of techniques speeds up inference and learning in CGMs by at least two orders of magnitude compared with the previous state-of-the-art while providing solutions of equal or better quality. We then show how this inference algorithm can be used in a CGM model for bird migration to reconstruct migration routes from birdwatcher reports.

Tao Sun is a PhD student in the School of Computer science, UMass Amherst, advised by Dan Sheldon. He is a member of Machine Learning and Data Science (MLDS) Lab. He is interested in machine learning and its applications, and is now working on the project BirdCast to develop novel methods for continent-scale bird migration prediction.

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Page last modified on October 29, 2013, at 01:16 PM