  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 linear/non-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.
