Gaussian Graphical Model Selection from Size Constrained Measurements

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

Abstract

In this paper, we introduce the problem of learning graphical models from size constrained measurements. This is inspired by a wide range of problems where one is unable to measure all the variables involved simultaneously. We propose notions of data requirement for this setting and then begin by considering an extreme case where one is allowed to only measure pairs of variables. For this setting we propose a simple algorithm and provide guarantees on its behavior. We then generalize to the case where one is allowed to measure up to r variables simultaneously, and draw connections to the field of combinatorial designs. Finally, we propose an interactive version of the proposed algorithm that is guaranteed to have significantly better data requirement on a wide range of realistic settings.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1302-1306
Number of pages5
ISBN (Electronic)9781538692912
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: Jul 7 2019Jul 12 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2019-July
ISSN (Print)2157-8095

Conference

Conference2019 IEEE International Symposium on Information Theory, ISIT 2019
CountryFrance
CityParis
Period7/7/197/12/19

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Keywords

  • active learning
  • combinatorial designs
  • Gaussian graphical models
  • sample complexity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Cite this

Dasarathy, G. (2019). Gaussian Graphical Model Selection from Size Constrained Measurements. In 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings (pp. 1302-1306). [8849299] (IEEE International Symposium on Information Theory - Proceedings; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2019.8849299