The matrix stick-breaking process for flexible multi-task learning

Published

Journal Article

In multi-task learning our goal is to design regression or classification models for each of the tasks and appropriately share information between tasks. A Dirichlet process (DP) prior can be used to encourage task clustering. However, the DP prior does not allow local clustering of tasks with respect to a subset of the feature vector without making independence assumptions. Motivated by this problem, we develop a new multitask-learning prior, termed the matrix stick-breaking process (MSBP), which encourages cross-task sharing of data. However, the MSBP allows separate clustering and borrowing of information for the different feature components. This is important when tasks are more closely related for certain features than for others. Bayesian inference proceeds by a Gibbs sampling algorithm and the approach is illustrated using a simulated example and a multi-national application.

Full Text

Duke Authors

Cited Authors

  • Xue, Y; Dunson, D; Carin, L

Published Date

  • August 23, 2007

Published In

  • Acm International Conference Proceeding Series

Volume / Issue

  • 227 /

Start / End Page

  • 1063 - 1070

Digital Object Identifier (DOI)

  • 10.1145/1273496.1273630

Citation Source

  • Scopus