9 citations · 24 across the 6 of their papers we have counts for
6 papers
Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs
Michael Chertkov, Andrew Gelfand, Jinwoo Shin
This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We present two novel problem…
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles
Sungsoo Ahn, Michael Chertkov, Andrew E. Gelfand +2
We study the Maximum Weight Matching (MWM) problem for general graphs through the max-product Belief Propagation (BP) and related Linear Programming (LP). The BP approach provides…
Belief Propagation for Linear Programming
Andrew Gelfand, Jinwoo Shin, Michael Chertkov
Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizin…
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
Max Welling, Andrew E. Gelfand, Alexander T. Ihler
We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) rec…
Generalized Belief Propagation on Tree Robust Structured Region Graphs
Andrew E. Gelfand, Max Welling
This paper provides some new guidance in the construction of region graphs for Generalized Belief Propagation (GBP). We connect the problem of choosing the outer regions of a LoopS…
BEEM : Bucket Elimination with External Memory
Kalev Kask, Rina Dechter, Andrew E. Gelfand
A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In thi…