@conference {bach:nips12, title = {Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization}, booktitle = {NeuRIPS}, year = {2012}, abstract = {

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints

}, author = {Stephen Bach and Matthias Broecheler and Lise Getoor and Dianne O{\textquoteright}Leary} }