Active learning for undirected graphical model selection

Divyanshu Vats, Robert D. Nowak, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an active learning algorithm that uses junction tree representations to adapt future measurements based on the information gathered from prior measurements. We prove that, under certain conditions, our active learning algorithm requires fewer scalar measurements than any passive algorithm to reliably estimate a graph. A range of numerical results validate our theory and demonstrates the benefits of active learning.

Original languageEnglish (US)
Pages (from-to)958-967
Number of pages10
JournalJournal of Machine Learning Research
Volume33
StatePublished - 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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