Active Task Selection for Multi-Task Learning.
Anastasia Pentina, Christoph H. Lampert · arXiv (Cornell University) · 2016
In this paper we consider the problem of multi-task learning, in which a learner is given a collection of prediction tasks that need to be solved. In contrast to previous work, we give up on the assumption that labeled training data is available for all tasks. Instead, we propose an active task selection framework, where based only on the unlabeled data, the learner can choose a, typically small, subset of tasks for which he gets some labeled examples. For the remaining tasks, which have no available annotation, solutions are found by transferring information from the selected tasks. We analyze two transfer strategies and develop generalization bounds for each of them. Based on this theoretical analysis we propose two algorithms for making the choice of labeled tasks in a principled way and show their effectiveness on synthetic and real data.