Asymmetric Multitask Learning Based on Task Relatedness and Loss

@inproceedings{Lee2016AsymmetricML,
  title={Asymmetric Multitask Learning Based on Task Relatedness and Loss},
  author={Giwoong Lee},
  year={2016}
}
We propose a novel multi-task learning method that minimizes the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric Multi-task Learning (AMTL). To tackle this problem, we couple multiple tasks via a sparse, directed regularization graph, that enforces each task parameter to be reconstructed as a sparse combination of other tasks selected based on the task-wise… CONTINUE READING