# 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} }

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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