Preprint
Oct 2026
Local-discrepancy ambiguity sets for distributionally robust optimization
In this paper, we introduce a new class of data-driven ambiguity sets for distributionally robust optimization (DRO) that aim to enhance the local inference accuracy of an unknown distribution. Building on optimal transport-based discrepancy measures, we depart from the conventional paradigm of monolithic ambiguity bal...
Ilias Papagoudis, Dimitris Boskos
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