Near-Optimal Bayesian Ambiguity Sets for Distributionally Robust Optimization

  title={Near-Optimal Bayesian Ambiguity Sets for Distributionally Robust Optimization},
  author={Vishal Gupta},
We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO) when the underlying distribution is defined by a finite-dimensional parameter. The key idea is to measure the relative size between a candidate ambiguity set and a specific, asymptotically optimal set. This asymptotically optimal set is provably the smallest convex ambiguity set that satisfies a particular Bayesian robustness guarantee with respect to… CONTINUE READING
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