TY - GEN
T1 - Approximation bounds for inference using cooperative cuts
AU - Jegelka, Stefanie
AU - Bilmes, Jeff
PY - 2011
Y1 - 2011
N2 - We analyze a family of probability distributions that are characterized by an embedded combinatorial structure. This family includes models having arbitrary treewidth and arbitrary sized factors. Unlike general models with such freedom, where the "most probable explanation" (MPE) problem is inapproximable, the combinatorial structure within our model, in particular the indirect use of submodularity, leads to several MPE algorithms that all have approximation guarantees.
AB - We analyze a family of probability distributions that are characterized by an embedded combinatorial structure. This family includes models having arbitrary treewidth and arbitrary sized factors. Unlike general models with such freedom, where the "most probable explanation" (MPE) problem is inapproximable, the combinatorial structure within our model, in particular the indirect use of submodularity, leads to several MPE algorithms that all have approximation guarantees.
UR - https://www.scopus.com/pages/publications/80053454291
M3 - Conference contribution
AN - SCOPUS:80053454291
SN - 9781450306195
T3 - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
SP - 577
EP - 584
BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
T2 - 28th International Conference on Machine Learning, ICML 2011
Y2 - 28 June 2011 through 2 July 2011
ER -