• Corpus ID: 174803510

Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter

@article{Hubis2019QuantitativeOM,
  title={Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter},
  author={Frances Ann Hubis and Wentao Wu and Ce Zhang},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.00299}
}
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML models. In this paper, we take a first step in an orthogonal direction towards automated quality management for human-in-the-loop ML application development. We build ease. ml/meter… 
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