More data means less inference: A pseudo-max approach to structured learning

David Sontag; Ofer Meshi; Tommi Jaakkola; Amir Globerson

More data means less inference: A pseudo-max approach to structured learning

David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson

Computer Science

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

Abstract

The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference are intractable. Here we show that it is possible to circumvent this difficulty when the distribution of training examples is rich enough, via a method similar in spirit to pseudo-likelihood. We show that our new method achieves consistency, and illustrate empirically that it indeed approaches the performance of exact methods when sufficiently large training sets are used.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 23
Subtitle of host publication	24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
State	Published - 2010
Event	24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 - Vancouver, BC, Canada Duration: Dec 6 2010 → Dec 9 2010

Publication series

Name	Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Other

Other	24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Country/Territory	Canada
City	Vancouver, BC
Period	12/6/10 → 12/9/10

ASJC Scopus subject areas

Information Systems

Cite this

Sontag, D., Meshi, O., Jaakkola, T., & Globerson, A. (2010). More data means less inference: A pseudo-max approach to structured learning. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

More data means less inference: A pseudo-max approach to structured learning. / Sontag, David; Meshi, Ofer; Jaakkola, Tommi et al.
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

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

Sontag, D, Meshi, O, Jaakkola, T & Globerson, A 2010, More data means less inference: A pseudo-max approach to structured learning. in Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010, 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010, Vancouver, BC, Canada, 12/6/10.

Sontag D, Meshi O, Jaakkola T, Globerson A. More data means less inference: A pseudo-max approach to structured learning. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

Sontag, David ; Meshi, Ofer ; Jaakkola, Tommi et al. / More data means less inference : A pseudo-max approach to structured learning. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

@inproceedings{368d6a3bd81047f1829daf179832508a,

title = "More data means less inference: A pseudo-max approach to structured learning",

abstract = "The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference are intractable. Here we show that it is possible to circumvent this difficulty when the distribution of training examples is rich enough, via a method similar in spirit to pseudo-likelihood. We show that our new method achieves consistency, and illustrate empirically that it indeed approaches the performance of exact methods when sufficiently large training sets are used.",

author = "David Sontag and Ofer Meshi and Tommi Jaakkola and Amir Globerson",

year = "2010",

language = "English (US)",

isbn = "9781617823800",

series = "Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010",

booktitle = "Advances in Neural Information Processing Systems 23",

note = "24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 ; Conference date: 06-12-2010 Through 09-12-2010",

}

TY - GEN

T1 - More data means less inference

T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

AU - Sontag, David

AU - Meshi, Ofer

AU - Jaakkola, Tommi

AU - Globerson, Amir

PY - 2010

Y1 - 2010

N2 - The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference are intractable. Here we show that it is possible to circumvent this difficulty when the distribution of training examples is rich enough, via a method similar in spirit to pseudo-likelihood. We show that our new method achieves consistency, and illustrate empirically that it indeed approaches the performance of exact methods when sufficiently large training sets are used.

AB - The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference are intractable. Here we show that it is possible to circumvent this difficulty when the distribution of training examples is rich enough, via a method similar in spirit to pseudo-likelihood. We show that our new method achieves consistency, and illustrate empirically that it indeed approaches the performance of exact methods when sufficiently large training sets are used.

UR - http://www.scopus.com/inward/record.url?scp=84860649508&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860649508&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84860649508

SN - 9781617823800

T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

BT - Advances in Neural Information Processing Systems 23

Y2 - 6 December 2010 through 9 December 2010

ER -

More data means less inference: A pseudo-max approach to structured learning

Abstract

Publication series

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this