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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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Gracia, Ibon (author), Boskos, D. (author), Laurenti, L. (author), Mazo, M. (author)We present a novel framework for formal control of uncertain discrete-time switched stochastic systems against probabilistic reach-avoid specifications. In particular, we consider stochastic systems with additive noise, whose distribution lies in an ambiguity set of distributions that are ε−close to a nominal one according to the Wasserstein...conference paper 2023
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Chaouach, L. (author), Oomen, T.A.E. (author), Boskos, D. (author)The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing...conference paper 2023
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Boskos, D. (author), Cortes, Jorge (author), Martínez, Sonia (author)This paper provides a data-driven solution to the problem of coverage control by which a team of robots aims to optimally deploy in a spatial region where certain event of interest may occur. This event is random and described by a probability density function, which is unknown and can only be learned by collecting data. In this work, we...conference paper 2023
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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