Multi-task Learning with Labeled and Unlabeled Tasks

@inproceedings{Pentina2017MultitaskLW,
  title={Multi-task Learning with Labeled and Unlabeled Tasks},
  author={Anastasia Pentina and Christoph H. Lampert},
  booktitle={ICML},
  year={2017}
}
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. Consequently, to solve all tasks, information must be transferred between tasks with labels and tasks without labels. Focusing on an instance-based transfer method we… CONTINUE READING
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