Conference Proceeding Article
Collective graphical models (CGMs) providea framework for reasoning about a populationof independent and identically distributed individualswhen only noisy and aggregate observationsare given. Previous approaches forinference in CGMs work on a junction-treerepresentation, thereby highly limiting theirscalability. To remedy this, we show how theBethe entropy approximation naturally arisesfor the inference problem in CGMs. We reformulatethe resulting optimization problem asa difference-of-convex functions program thatcan capture different types of CGM noisemodels. Using the concave-convex procedure,we then develop a scalable messagepassingalgorithm. Empirically, we showour approach is highly scalable and accuratefor large graphs, more than an orderof-magnitudefaster than a generic optimizationsolver, and is guaranteed to converge unlikethe previous message-passing approachNLBP that fails in several loopy graphs.
Intelligent Systems and Decision Analytics
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain
City or Country
NGUYEN DUC THIEN; Akshat KUMAR; LAU, Hoong Chuin; and SHELDON, Daniel.
Approximate inference using DC programming for collective graphical models. (2016). Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. 51, 685-693. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3400
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