Multi-task learning aims at improving the performance of one learning task with the help of other related tasks. It is particularly useful when each task has very limited labeled data. A central issue in multi-task learning is to learn and exploit the relationships between tasks. In this paper, we generalize boosting to the multi-task learning setting and propose a method called multi-task boosting (MTBoost). Different tasks in MTBoost share the same base learners but with different weights which are related to the estimated task relationships in each iteration. In MTBoost, unlike ordinary boosting methods, the base learners, weights and task covariances are learned together in an integrated fashion using an alternating optimization procedure. We conduct theoretical analysis on the convergence of MTBoost and also empirical analysis comparing it with several related methods.
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