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Poster
Barrier Frank-Wolfe for Marginal Inference
Rahul G Krishnan · Simon Lacoste-Julien · David Sontag

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #48
in Poster Session »

We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gradient method (Frank-Wolfe) and moves pseudomarginals within the marginal polytope through repeated maximum a posteriori (MAP) calls. This modular structure enables us to leverage black-box MAP solvers (both exact and approximate) for variational inference, and obtains more accurate results than tree-reweighted algorithms that optimize over the local consistency relaxation. Theoretically, we bound the sub-optimality for the proposed algorithm despite the TRW objective having unbounded gradients at the boundary of the marginal polytope. Empirically, we demonstrate the increased quality of results found by tightening the relaxation over the marginal polytope as well as the spanning tree polytope on synthetic and real-world instances.

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Author Information

Rahul G Krishnan (New York University)
Simon Lacoste-Julien (INRIA)
David Sontag (NYU)

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