Distributed variance regularized Multitask Learning

Past research on Multitask Learning (MTL) has focused mainly on devising adequate regularizers and less on their scalability. In this paper, we present a method to scale up MTL methods which penalize the variance of the task weight vectors. The method builds upon the alternating direction method of multipliers to decouple the variance regularizer. It can be efficiently implemented by a distributed algorithm, in which the tasks are first independently solved and subsequently corrected to pool information from other tasks. We show that the method works well in practice and convergences in few distributed iterations. Furthermore, we empirically observe that the number of iterations is nearly independent of the number of tasks, yielding a computational gain of O(T) over standard solvers. We also present experiments on a large URL classification dataset, which is challenging both in terms of volume of data points and dimensionality. Our results confirm that MTL can obtain superior performance over either learning a common model or independent task learning.