Mohammad Reza Belbasi

It’s All in the Mix: Wasserstein Classification and Regression with Mixed Features

  • Mohammad Reza Belbasi
  • Aras Selvi
  • Wolfram Wiesemann
    • Categories Optimization in Data Science, Robust Optimization Tags , , ,

    Problem definition: A key challenge in supervised learning is data scarcity, which can cause prediction models to overfit to the training data and perform poorly out of sample. A contemporary approach to combat overfitting is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, … Read more

    Wasserstein Logistic Regression with Mixed Features

  • Aras Selvi
  • Mohammad Reza Belbasi
  • Martin Haugh
  • Wolfram Wiesemann
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Robust Optimization Tags , ,

    Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that distributionally robust logistic regression with mixed (i.e., numerical and categorical) features, despite amounting … Read more