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Approximate Inference in Collective Graphical Models

TitleApproximate Inference in Collective Graphical Models
Publication TypeConference Paper
Year of Publication2013
AuthorsSheldon, D., T. Sun, A. Kumar, and T. G. Dietterich
Conference NameInternational Conference on Machine Learning, ICML-2013
Pagination1004–1012
Date Published06/2013
Conference LocationAtlanta
Abstract

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.

URLhttp://jmlr.org/proceedings/papers/v28/sheldon13.html