Repository logo
 

Revisiting the limits of MAP inference by MWSS on perfect graphs


Change log

Authors

Abstract

A recent, promising approach to identifying a configuration of a discrete graphical model with highest probability (termed MAP inference) is to reduce the problem to finding a maximum weight stable set (MWSS) in a derived weighted graph, which, if perfect, allows a solution to be found in polynomial time. Weller and Jebara (2013) investigated the class of binary pairwise models where this method may be applied. However, their analysis made a seemingly innocuous assumption which simplifies analysis but led to only a subset of possible reparameterizations being considered. Here we introduce novel techniques and consider all cases, demonstrating that this greatly expands the set of tractable models. We provide a simple, exact characterization of the new, enlarged set and show how such models may be efficiently identified, thus settling the power of the approach on this class.

Description

This is the author accepted manuscript. The final version is available from MIT Press via http://jmlr.org/proceedings/papers/v38/weller15.pdf

Keywords

Journal Title

Journal of Machine Learning Research

Conference Name

Journal ISSN

1532-4435
1533-7928

Volume Title

Publisher

MIT Press

Publisher DOI