Anytime Marginal MAP Inference
  This paper presents a new anytime algorithm for the marginal MAP problem in graphical models. The algorithm is described in detail, its complexity and convergence rate are studied, and relations to previous theoretical results for the problem are discussed. It is shown that the algorithm runs in polynomial-time if the underlying graph of the model has bounded tree-width, and that it provides guarantees to the lower and upper bounds obtained within a fixed amount of computational resources. Experiments with both real and synthetic generated models highlight its main characteristics and show that it compares favorably against Park and Darwiche's systematic search, particularly in the case of problems with many MAP variables and moderate tree-width. 
Denis Maua (IDSIA), Cassio De Campos (IDSIA)
cs.AI stat.ML