Learning and inference with constraints

Ming Wei Chang, Lev Ratinov, Nicholas Rizzolo, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments arc possible. However, incorporating non-local dependencies in a probabilistic model can lead to intractable training and inference. This paper presents Constraints Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. We further show that declarative constraints can be used to take advantage of unlabeled data when training the probabilistic model.

Original languageEnglish (US)
Title of host publicationAAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference
Pages1513-1518
Number of pages6
StatePublished - Dec 23 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Chang, M. W., Ratinov, L., Rizzolo, N., & Roth, D. (2008). Learning and inference with constraints. In AAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference (pp. 1513-1518). (Proceedings of the National Conference on Artificial Intelligence; Vol. 3).