Belief propagation is a widely used, incomplete optimization algorithm whose main
theoretical properties hold only under the assumption that beliefs are not equal …

Max-sum is a version of Belief Propagation, used for solving DCOPs. In tree-structured
problems, Max-sum converges to the optimal solution in linear time. Unfortunately, when the …

We consider the maximum-a-posteriori inference problem in discrete graphical models and
study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in …

Marginal MAP inference involves making MAP predictions in systems defined with latent
variables or missing information. It is significantly more difficult than pure marginalization …

Belief propagation, an algorithm for solving problems represented by graphical models, has
long been known to converge to the optimal solution when the graph is a tree. When the …

Many machine learning tasks require fitting probabilistic models over structured objects,
such as pixel grids, matchings, and graph edges. Maximum likelihood estimation (MLE) for …

Gaussian Process (GP) formulation of continuous-time trajectory offers a fast solution to the
motion planning problem via probabilistic inference on factor graph. However, often the …

Computing the MAP assignment in graphical models is generally intractable. As a result, for
discrete graphical models, the MAP problem is often approximated using linear …

Abstract We apply the Min-Sum message-passing protocol to solve the consensus problem
in distributed optimization. We show that while the ordinary Min-Sum algorithm does not …

Max-sum is a version of belief propagation that was adapted for solving distributed
constraint optimization problems (DCOPs). It has been studied theoretically and empirically …