Title: Co-Clustering for Multitask Learning,
Abstract: This paper presents a new multitask learning framework that learns a shared
representation among the tasks, incorporating both task and feature clusters.
The jointly-induced clusters yield a shared latent subspace where task
relationships are learned more effectively and more generally than in
state-of-the-art multitask learning methods. The proposed general framework
enables the derivation of more specific or restricted state-of-the-art
multitask methods. The paper also proposes a highly-scalable multitask learning
algorithm, based on the new framework, using conjugate gradient descent and
generalized \textit{Sylvester equations}. Experimental results on synthetic and
benchmark datasets show that the proposed method systematically outperforms
several state-of-the-art multitask learning methods.