Flexible Modeling of Latent Task Structures in Multitask Learning

TL;DR

This paper introduces a flexible, nonparametric Bayesian model for multitask learning that automatically learns the latent task structure from data, accommodating various existing models and discovering new structures.

Contribution

It proposes a novel nonparametric Bayesian approach with a mixture of factor analyzers for adaptive latent task structure learning in multitask learning.

Findings

Effective on synthetic datasets

Improves performance on real-world tasks

Captures diverse task relationships

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.