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arXiv · 1206.6486

Flexible Modeling of Latent Task Structures in Multitask Learning

Alexandre Passos·Piyush Rai·Jacques Wainer·Hal Daume III
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Abstract

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.

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Alexandre Passos, Piyush Rai, Jacques Wainer, Hal Daume III. 2012-06-27. Flexible Modeling of Latent Task Structures in Multitask Learning. https://arxiv.org/abs/1206.6486

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