Uncertainty in Artificial Intelligence
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Optimization of Structured Mean Field Objectives
Alexandre Bouchard-Cote, Michael Jordan
In intractable, undirected graphical models, an intuitive way of creating structured mean field approximations is to select an acyclic tractable subgraph. We show that the hardness of computing the objective function and gradient of the mean field objective qualitatively depends on a simple graph property. If the tractable subgraph has this property- we call such subgraphs v-acyclic-a very fast block coordinate ascent algorithm is possible. If not, optimization is harder, but we show a new algorithm based on the construction of an auxiliary exponential family that can be used to make inference possible in this case as well. We discuss the advantages and disadvantages of each regime and compare the algorithms empirically.
Keywords: null
Pages: 67-74
PS Link:
PDF Link: /papers/09/p67-bouchard-cote.pdf
AUTHOR = "Alexandre Bouchard-Cote and Michael Jordan",
TITLE = "Optimization of Structured Mean Field Objectives",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "67--74"

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