Frameworks and Results in Distributionally Robust Optimization [PDF]
Open Journal of Mathematical Optimization, 2022 The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these ...
Rahimian, Hamed, Mehrotra, Sanjay
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Distributionally Robust Convex Optimization [PDF]
Operations Research, 2014 Distributionally robust optimization is a paradigm for decision making under uncertainty where the uncertain problem data are governed by a probability distribution that is itself subject to uncertainty. The distribution is then assumed to belong to an ambiguity set comprising all distributions that are compatible with the decision maker’s prior ...
Melvyn Sim +2 more
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Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space–Air–Ground Integrated Network [PDF]
Sensors, 2023 As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ...
Zhiyuan Li, Pinrun Chen
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Sinkhorn Distributionally Robust Conditional Quantile Prediction with Fixed Design [PDF]
EntropyThis paper proposes a novel data-driven distributionally robust framework for conditional quantile prediction under the fixed design setting of the covariates, which we refer to as Sinkhorn distributionally robust conditional quantile prediction.
Guohui Jiang, Tiantian Mao
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From Data to Decisions: Distributionally Robust Optimization Is Optimal [PDF]
Management Science, 2021 We study stochastic programs where the decision maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transforms the data to an estimate of the expected cost function under the unknown data-generating ...
Peyman Mohajerin Esfahani +2 more
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Robust topology and dispatch optimization for renewable distribution networks with electric vehicle mobility uncertainty [PDF]
Scientific ReportsThe increasing interdependence between power distribution networks and transportation systems introduces unprecedented operational complexity under high renewable penetration and stochastic electric vehicle (EV) mobility.
Liang Xu +4 more
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Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic. [PDF]
Comput Ind Eng, 2022 Ash C +3 more
europepmc +2 more sources
Wasserstein Distributionally Robust Optimization and Variation Regularization
Operations ResearchThis paper builds a bridge between two area in optimization and machine learning by establishing a general connection between Wasserstein distributional robustness and variation regularization. It helps to demystify the empirical success of Wasserstein distributionally robust optimization and devise new regularization schemes for machine learning.
Anton Kleywegt, Xi Chen, Rui Gao
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Distributionally Robust Optimization and Robust Statistics
Statistical ScienceWe review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss between the training and deployment environments. Many well-known estimators in statistics and machine learning (e.g.
Blanchet, Jose +3 more
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Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro 0001 +2 more
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