Multi-Task Learning with Labeled and Unlabeled Tasks

ICML 2017 Anastasia PentinaChristoph H. Lampert

In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided... (read more)

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