Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#257985] of David B. Dunson


Papers Published

  1. Wang, C; An, Q; Carin, L; Dunson, DB, Multi-task classification with infinite local experts, IEEE International Conference on Acoustics Speech and Signal Processing (2009), pp. 1569-1572, ISSN 1520-6149 [doi]
    (last updated on 2017/12/13)

    We propose a multi-task learning (MTL) framework for nonlinear classification, based on an infinite set of local experts in feature space. The usage of local experts enables sharing at the expert-level, encouraging the borrowing of information even if tasks are similar only in subregions of feature space. A kernel stick-breaking process (KSBP) prior is imposed on the underlying distribution of class labels, so that the number of experts is inferred in the posterior and thus model selection issues are avoided. The MTL is implemented by imposing a Dirichlet process (DP) prior on a layer above the task- dependent KSBPs. ©2009 IEEE.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320