explainability

Computing Counterfactual Explanations for Linear Optimization: A New Class of Bilevel Models and a Tailored Penalty Alternating Direction Method

  • Henri Lefebvre
  • Martin Schmidt
    • Categories Energy, Nonlinear Optimization, Optimization in Data Science Tags , , , ,

    Explainable artificial intelligence is one of the most important trends in modern machine-learning research. The idea is to explain the outcome of a model by presenting a certain change in the input of the model so that the outcome changes significantly. In this paper, we study this question for linear optimization problems as an automated … Read more

    Enhancing explainability of stochastic programming solutions via scenario and recourse reduction

  • Tushar Rathi
  • Rishabh Gupta
  • Jose M. Pinto
  • Qi Zhang
    • Categories Stochastic Programming Tags , , ,

    Stochastic programming (SP) is a well-studied framework for modeling optimization problems under uncertainty. However, despite the significant advancements in solving large SP models, they are not widely used in industrial practice, often because SP solutions are difficult to understand and hence not trusted by the user. Unlike deterministic optimization models, SP models generally involve recourse … Read more