Multi-task sparse structure learning

André R. Gonçalves, Puja Das, Soumyadeep Chatterjee, Vidyashankar Sivakumar, Fernando J. Von Zuben, Arindam Banerjee

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

25 Scopus citations

Abstract

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification. We also consider the problem of combining climate model outputs for better projections of future climate, with focus on temperature in South America, and show that the proposed model outperforms several existing methods for the problem.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages451-460
Number of pages10
ISBN (Electronic)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Country/TerritoryChina
CityShanghai
Period11/3/1411/7/14

Keywords

  • Algorithms

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