Title | Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization |
Publication Type | Conference Paper |
Year of Publication | 2012 |
Authors | Bach, S, Broecheler, M, Getoor, L, O'Leary, D |
Conference Name | NeuRIPS |
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 |