data-driven decision making

Maximum Likelihood Probability Measures over Sets and Applications to Data-Driven Optimization

  • Juan Borrero
  • Denis Saure
    • Categories Applications - OR and Management Sciences, Optimization in Data Science, Robust Optimization Tags , ,

    \(\) Motivated by data-driven approaches to sequential decision-making under uncertainty, we study maximum likelihood estimation of a distribution over a general measurable space when, unlike traditional setups, realizations of the underlying uncertainty are not directly observable but instead are known to lie within observable sets. While extant work studied the special cases when the observed … Read more

    Robust Generalization despite Distribution Shift via Minimum Discriminating Information

  • Andreas Krause
  • Daniel Kuhn
  • Tobias Sutter
    • Categories Robust Optimization Tags , , , , ,

    Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribution. We employ the principle of minimum discriminating information to embed the available prior knowledge, and use … Read more

    A General Framework for Optimal Data-Driven Optimization

  • Daniel Kuhn
  • Tobias Sutter
  • Bart Paul Gerard Van Parys
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be expressed as the minimizer of a surrogate optimization model constructed from the data. The quality of a data-driven decision … Read more

    From Predictive to Prescriptive Analytics

  • Dimitris Bertsimas
  • Nathan Kallus
    • Categories Applications - OR and Management Sciences Tags , , , , ,

    In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications … Read more