@InProceedings{daume09hiermtl,
  author =       {Hal {Daum\'e III}},
  title =        {Bayesian Multitask Learning with Latent Hierarchies},
  booktitle =    {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year =         {2009},
  address =      {Montreal, Canada},
  abstract =     {
    We learn multiple hypotheses for related tasks under a latent
    hierarchical relationship between tasks.  We exploit the intuition
    that for \emph{domain adaptation}, we wish to share classifier
    structure, but for \emph{multitask learning}, we wish to share
    covariance structure.  Our hierarchical model is seen to subsume
    several previously proposed multitask learning models and performs
    well on three distinct real-world data sets.
  },
  keywords = {ml bayes da},
  tagline = {We show that hierarchical multitask learning can be accomplished even when the hierarchical structure is not known in advance. We use the <a href="#daume07coalescent">coalescent</a> to make this happen.},
  url = {http://pub.hal3.name/#daume09hiermtl}
}