Flexible Modeling of Latent Task Structures in Multitask Learning

Understand

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.