### Abstract

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.

Original language | English (US) |
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Title of host publication | ICML 2010 - Proceedings, 27th International Conference on Machine Learning |

Pages | 783-790 |

Number of pages | 8 |

State | Published - 2010 |

Event | 27th International Conference on Machine Learning, ICML 2010 - Haifa, Israel Duration: Jun 21 2010 → Jun 25 2010 |

### Other

Other | 27th International Conference on Machine Learning, ICML 2010 |
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Country | Israel |

City | Haifa |

Period | 6/21/10 → 6/25/10 |

### Fingerprint

### ASJC Scopus subject areas

- Artificial Intelligence
- Education

### Cite this

*ICML 2010 - Proceedings, 27th International Conference on Machine Learning*(pp. 783-790)

**Learning efficiently with approximate inference via dual losses.** / Meshi, Ofer; Sontag, David; Jaakkola, Tommi; Globerson, Amir.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*ICML 2010 - Proceedings, 27th International Conference on Machine Learning.*pp. 783-790, 27th International Conference on Machine Learning, ICML 2010, Haifa, Israel, 6/21/10.

}

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

SN - 9781605589077

SP - 783

EP - 790

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

ER -