A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks
Artificial Intelligence
2012-03-19 v1
Abstract
The paper introduces k-bounded MAP inference, a parameterization of MAP inference in Markov logic networks. k-Bounded MAP states are MAP states with at most k active ground atoms of hidden (non-evidence) predicates. We present a novel delayed column generation algorithm and provide empirical evidence that the algorithm efficiently computes k-bounded MAP states for meaningful real-world graph matching problems. The underlying idea is that, instead of solving one large optimization problem, it is often more efficient to tackle several small ones.
Cite
@article{arxiv.1203.3499,
title = {A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks},
author = {Mathias Niepert},
journal= {arXiv preprint arXiv:1203.3499},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)