Uncertainty in Artificial Intelligence
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Lifted Tree-Reweighted Variational Inference
Hung Bui, Tuyen Huynh, David Sontag
We analyze variational inference for highly sym- metric graphical models such as those arising from first-order probabilistic models. We first show that for these graphical models, the tree- reweighted variational objective lends itself to a compact lifted formulation which can be solved much more efficiently than the standard TRW formulation for the ground graphical model. Compared to earlier work on lifted belief prop- agation, our formulation leads to a convex op- timization problem for lifted marginal inference and provides an upper bound on the partition function. We provide two approaches for im- proving the lifted TRW upper bound. The first is a method for efficiently computing maxi- mum spanning trees in highly symmetric graphs, which can be used to optimize the TRW edge ap- pearance probabilities. The second is a method for tightening the relaxation of the marginal poly- tope using lifted cycle inequalities and novel ex- changeable cluster consistency constraints.
Pages: 92-101
PS Link:
PDF Link: /papers/14/p92-bui.pdf
AUTHOR = "Hung Bui and Tuyen Huynh and David Sontag",
TITLE = "Lifted Tree-Reweighted Variational Inference",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "92--101"

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