Marginal Inference in MRFs using Frank-Wolfe

By David Belanger, D. Sheldon, Andrew McCallum. 2013

18 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. David A. Sontag
2. Rahul G. Krishnan
3. Simon Lacoste-Julien
4. André F. T. Martins
5. Claire Cardie
6. Vlad Niculae
7. Mathieu Blondel
8. Stephan Mandt

Citations in the corpus

• Barrier Frank-Wolfe for Marginal Inference (2015, methodology): “The FW linear subproblem here is thus equivalent to performing MAP inference in a graphical model with potentials θ̃ (Belanger et al., 2013), as the vertices of the marginal polytope are in 1-1 correspondence with valid joint assignments to the random variables of the MRF, and the solution of a line”
• SparseMAP: Differentiable Sparse Structured Inference (2018, background): “One family of such solvers is based on the conditional gradient (CG) algorithm (Frank & Wolfe, 1956; Lacoste-Julien & Jaggi, 2015), considered in prior work for solving approximations of the marginal inference problem (Belanger et al., 2013; Krishnan et al., 2015).”