TY - GEN

T1 - Learning efficiently with approximate inference via dual losses

AU - Meshi, Ofer

AU - Sontag, David

AU - Jaakkola, Tommi

AU - Globerson, Amir

PY - 2010

Y1 - 2010

N2 - Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for structured prediction (e.g., cutting-plane, subgradient methods, perceptron) repeatedly make predictions for some of the data points. These approaches are computationally demanding because each prediction involves solving a linear program to optimally. We present a scalable algorithm for learning for structured prediction. The main idea is to instead solve the dual of the structured prediction loss. We formulate the learning task as a convex minimization over both the weights and the dual variables corresponding to each data point. As a result, we can begin to optimize the weights even before completely solving any of the individual prediction problems. We show how the dual variables can be efficiently optimized using co-ordinate descent. Our algorithm is competitive with state-of-the-art methods such as stochastic subgradient and cutting-plane.

AB - Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation. Previous approaches for learning for structured prediction (e.g., cutting-plane, subgradient methods, perceptron) repeatedly make predictions for some of the data points. These approaches are computationally demanding because each prediction involves solving a linear program to optimally. We present a scalable algorithm for learning for structured prediction. The main idea is to instead solve the dual of the structured prediction loss. We formulate the learning task as a convex minimization over both the weights and the dual variables corresponding to each data point. As a result, we can begin to optimize the weights even before completely solving any of the individual prediction problems. We show how the dual variables can be efficiently optimized using co-ordinate descent. Our algorithm is competitive with state-of-the-art methods such as stochastic subgradient and cutting-plane.

UR - http://www.scopus.com/inward/record.url?scp=77956556288&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956556288&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:77956556288

SN - 9781605589077

T3 - ICML 2010 - Proceedings, 27th International Conference on Machine Learning

SP - 783

EP - 790

BT - ICML 2010 - Proceedings, 27th International Conference on Machine Learning

T2 - 27th International Conference on Machine Learning, ICML 2010

Y2 - 21 June 2010 through 25 June 2010

ER -