Tiantian Mao

When Wasserstein DRO Reduces Exactly: Complete Characterization, Projection Equivalence, and Regularization

  • Chenling Huang
  • Jonathan Yu-Meng Li
  • Tiantian Mao
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Wasserstein distributionally robust optimization (DRO), a leading paradigm in data-driven decision-making, entails the evaluation of worst-case risk over a high-dimensional Wasserstein ball–a major computational burden. In this paper, we study when the worst-case risk problem admits an exact reduction to the evaluation of risk over a one-dimensional projected Wasserstein ball—a property we refer to as … Read more

    A General Wasserstein Framework for Data-driven Distributionally Robust Optimization: Tractability and Applications

  • Jonathan Yu-Meng Li
  • Tiantian Mao
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    Data-driven distributionally robust optimization is a recently emerging paradigm aimed at finding a solution that is driven by sample data but is protected against sampling errors. An increasingly popular approach, known as Wasserstein distributionally robust optimization (DRO), achieves this by applying the Wasserstein metric to construct a ball centred at the empirical distribution and finding … Read more

    Distributionally Robust Optimization under Distorted Expectations

  • Jun Cai
  • Jonathan Yu-Meng Li
  • Tiantian Mao
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , , ,

    Distributionally robust optimization (DRO) has arose as an important paradigm to address the issue of distributional ambiguity in decision optimization. In its standard form, DRO seeks an optimal solution against the worst-possible expected value evaluated based on a set of candidate distributions. In the case where a decision maker is not risk neutral, the most … Read more