Distributionally Robust Logistic Regression

@inproceedings{ShafieezadehAbadeh2015DistributionallyRL,
  title={Distributionally Robust Logistic Regression},
  author={Soroosh Shafieezadeh-Abadeh and Peyman Mohajerin Esfahani and Daniel Kuhn},
  booktitle={NIPS},
  year={2015}
}
This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown datagenerating distribution with high confidence. We then formulate a distributionally robust logistic regression model that minimizes a worst-case expected logloss… CONTINUE READING
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