Math @ Duke

Publications [#257979] of David B. Dunson
search www.stat.duke.edu.Papers Published
 Xue, Y; Dunson, D; Carin, L, The matrix stickbreaking process for flexible multitask learning,
ACM International Conference Proceeding Series, vol. 227
(2007),
pp. 10631070 [doi]
(last updated on 2018/08/18)
Abstract: In multitask 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 multitasklearning prior, termed the matrix stickbreaking process (MSBP), which encourages crosstask 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 multinational application.


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