June 11, 2024, 4:45 a.m. | Mohammed Rayyan Sheriff, Peyman Mohajerin Esfahani

stat.ML updates on arXiv.org arxiv.org

arXiv:2306.03202v2 Announce Type: replace
Abstract: This article focuses on a class of distributionally robust optimization (DRO) problems where, unlike the growing body of the literature, the objective function is potentially nonlinear in the distribution. Existing methods to optimize nonlinear functions in probability space use the Frechet derivatives, which present both theoretical and computational challenges. Motivated by this, we propose an alternative notion for the derivative and corresponding smoothness based on Gateaux (G)-derivative for generic risk measures. These concepts are explained …

abstract article arxiv challenges class computational cs.lg derivatives distribution function functions literature math.oc optimization probability replace robust space stat.ml type

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