Multi-task Sparse Structure Learning
This work addresses the challenge of estimating unknown task relationships in multi-task learning, which is incremental as it builds on existing sparse structure learning techniques.
The paper tackles the problem of learning task relationship structures in multi-task learning by proposing a novel family of models that jointly estimate structure and parameters using alternating minimization, and demonstrates effectiveness on synthetic and benchmark datasets, outperforming existing methods in climate projection applications with concrete improvements in temperature predictions for South America.
Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification. We also consider the problem of combining climate model outputs for better projections of future climate, with focus on temperature in South America, and show that the proposed model outperforms several existing methods for the problem.