1 code implementation • 7 Mar 2023 • Soroosh Shafieezadeh-Abadeh, Liviu Aolaritei, Florian Dörfler, Daniel Kuhn
We study optimal transport-based distributionally robust optimization problems where a fictitious adversary, often envisioned as nature, can choose the distribution of the uncertain problem parameters by reshaping a prescribed reference distribution at a finite transportation cost.
no code implementations • 27 Jun 2022 • Liviu Aolaritei, Soroosh Shafiee, Florian Dörfler
Wasserstein distributionally robust optimization has recently emerged as a powerful framework for robust estimation, enjoying good out-of-sample performance guarantees, well-understood regularization effects, and computationally tractable reformulations.