Discovering hidden variables: A structure-based approach

Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations


A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this paper, we address the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models. A very natural approach is to search for "structural signatures" of hidden variables - substructures in the learned network that tend to suggest the presence of a hidden variable. We make this basic idea concrete, and show how to integrate it with structure-search algorithms. We evaluate this method on several synthetic and real-life datasets, and show that it performs surprisingly well.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PublisherNeural information processing systems foundation
ISBN (Print)0262122413, 9780262122412
StatePublished - 2001
Event14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States
Duration: 27 Nov 20002 Dec 2000

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference14th Annual Neural Information Processing Systems Conference, NIPS 2000
Country/TerritoryUnited States
CityDenver, CO


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