Arrow Research search
Back to ICML

ICML 2016

Uprooting and Rerooting Graphical Models

Conference Paper Accepted Papers Artificial Intelligence · Machine Learning

Abstract

We show how any binary pairwise model may be “uprooted” to a fully symmetric model, wherein original singleton potentials are transformed to potentials on edges to an added variable, and then “rerooted” to a new model on the original number of variables. The new model is essentially equivalent to the original model, with the same partition function and allowing recovery of the original marginals or a MAP configuration, yet may have very different computational properties that allow much more efficient inference. This meta-approach deepens our understanding, may be applied to any existing algorithm to yield improved methods in practice, generalizes earlier theoretical results, and reveals a remarkable interpretation of the triplet-consistent polytope.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
International Conference on Machine Learning
Archive span
1993-2025
Indexed papers
16471
Paper id
97242199983566462
v2026.09.13