Distributed MAP Inference for Undirected Graphical Models
published: Jan. 13, 2011, recorded: December 2010, views: 267
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In this work, we distribute the MCMC-based MAP inference using the Map-Reduce framework. The variables are assigned randomly to machines, which leads to some factors that neighbor vari- ables on separate machines. Parallel MCMC-chains are initiated using proposal distributions that only suggest local changes such that factors that lie across machines are not examined. After a fixed number of samples on each machine, we redistribute the variables amongst the machines to enable proposals across variables that were on different machines. To demonstrate the distribution strategy on a real-world information extraction application, we model the task of cross-document coreference.
Download slides: nipsworkshops2010_singh_dmapi_01.pdf (1.7 MB)
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