Accession Number:

AD1042340

Title:

Flexible Modeling of Latent Task Structures in Multitask Learning

Descriptive Note:

Conference Paper

Corporate Author:

University of Massachusetts Amherst United States

Report Date:

2012-06-26

Pagination or Media Count:

8.0

Abstract:

Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the right latent task structure should be learned in a data-driven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures e.g, mean-regularized tasks, clustered tasks, low-rank or linearnon-linear subspace assumption on tasks, etc.. Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and real- world datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.

Subject Categories:

  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE