TY - GEN

T1 - How hard is inference for structured prediction?

AU - Globerson, Amir

AU - Roughgarden, Tim

AU - Sontag, David

AU - Yildirim, Cafer

N1 - Funding Information:
AG was supported by ISF Centers of Excellence grant 1789/11, TR by NSF grant CCF-1215965, and DS by ONR #N00014-13-1-0646 and NSF CAREER award #1350965.
Publisher Copyright:
© Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.

PY - 2015

Y1 - 2015

N2 - Structured prediction tasks in machine learning involve the simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise elements, each depending on two specific labels. The goal of this paper is to develop a theoretical explanation of the empirical effectiveness of heuristic inference algorithms for solving such structured prediction problems. We study the minimum-achievable expected Hamming error in such problems, highlighting the case of 2D grid graphs, which are common in machine vision applications. Our main theorems provide tight upper and lower bounds on this error, as well as a polynomial-time algorithm that achieves the bound.

AB - Structured prediction tasks in machine learning involve the simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise elements, each depending on two specific labels. The goal of this paper is to develop a theoretical explanation of the empirical effectiveness of heuristic inference algorithms for solving such structured prediction problems. We study the minimum-achievable expected Hamming error in such problems, highlighting the case of 2D grid graphs, which are common in machine vision applications. Our main theorems provide tight upper and lower bounds on this error, as well as a polynomial-time algorithm that achieves the bound.

UR - http://www.scopus.com/inward/record.url?scp=84969961954&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969961954&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84969961954

T3 - 32nd International Conference on Machine Learning, ICML 2015

SP - 2171

EP - 2180

BT - 32nd International Conference on Machine Learning, ICML 2015

A2 - Bach, Francis

A2 - Blei, David

PB - International Machine Learning Society (IMLS)

T2 - 32nd International Conference on Machine Learning, ICML 2015

Y2 - 6 July 2015 through 11 July 2015

ER -