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Boskos, D. (author), Cortes, Jorge (author), Martinez, Sonia (author)This paper builds Wasserstein ambiguity sets for the unknown probability distribution of dynamic random variables leveraging noisy partial-state observations. The constructed ambiguity sets contain the true distribution of the data with quantifiable probability and can be exploited to formulate robust stochastic optimization problems with out...journal article 2024
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Chaouach, L. (author), Boskos, D. (author), Oomen, T.A.E. (author)Ambiguity sets of probability distributions are a prominent tool to hedge against distributional uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein ambiguity sets for data-driven optimization problems. The method exploits independence between the distribution components to introduce structure in the...conference paper 2022
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Boskos, D. (author), Cortes, Jorge (author), Martinez Sandez, S. (author)This paper introduces a spectral parameterization of ambiguity sets to hedge against distributional uncertainty in stochastic optimization problems. We build an ambiguity set of probability densities around a histogram estimator, which is constructed by independent samples from the unknown distribution. The densities in the ambiguity set are...conference paper 2022