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Exponential family graph matching and ranking

Date

Authors

Petterson, James

Caetano, Tiberio

McAuley, Julian

Yu, Jin

Publisher

MIT Press

Abstract

We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a posteriori estimation in exponential families with sufficient statistics that encode permutations and data features. Although

Keywords

Keywords: Benchmark datasets; Bipartite graphs; Data feature; Document ranking; Exponential family; General model; Graph matchings; Large graphs; Matching models; Maximum a posteriori estimation; Runtimes; Sample sizes; Sufficient statistics; Computer vision; Sampl

URI

http://hdl.handle.net/1885/56853

Collections

ANU Research Publications

Source

Proceedings of The 23rd Annual Conference on Neural Information Processing Systems (NIPS 23)

Type

Conference paper

Restricted until

2037-12-31

Downloads

File

Description

01_Petterson_Exponential_family_graph_2009.pdf (638.52 KB)

02_Petterson_Exponential_family_graph_2009.pdf (17.51 KB)