Corpus ID: 3115038

Satisfying Real-world Goals with Dataset Constraints

@article{Goh2016SatisfyingRG,
  title={Satisfying Real-world Goals with Dataset Constraints},
  author={G. Goh and Andrew Cotter and M. Gupta and M. Friedlander},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.07558}
}
  • G. Goh, Andrew Cotter, +1 author M. Friedlander
  • Published 2016
  • Computer Science, Mathematics
  • ArXiv
  • The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets. For example, one may require a classifier to also make positive predictions at some specified rate for some subpopulation (fairness), or to achieve a specified empirical recall. Other real-world goals include reducing churn with respect to a previously deployed model, or stabilizing online training. In this paper we propose handling multiple… CONTINUE READING
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