• Corpus ID: 254562281

Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks

@inproceedings{Liu2022DistributionEN,
  title={Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks},
  author={Lang Liu and Mahdi Milani Fard and Sen Zhao},
  year={2022}
}
We propose Distribution Embedding Networks (DEN) for classification with small data. In the same spirit of meta-learning, DEN learns from a diverse set of training tasks with the goal to generalize to unseen target tasks. Unlike existing approaches which require the inputs of training and target tasks to have the same dimension with possibly similar distributions, DEN allows training and target tasks to live in heterogeneous input spaces. This is especially useful for tabular-data tasks where… 
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