Flexible Modeling of Latent Task Structures in Multitask Learning
University of Massachusetts Amherst United States
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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 linearnon-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.
- Statistics and Probability