Results 221 to 230 of about 765 (256)
Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networks. [PDF]
PLoS OneWang X +5 more
europepmc +1 more source
Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning Models. [PDF]
Cancer MedDo H, Ranganath R, Murray K, Nayan M.
europepmc +1 more source
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.
Enlu Zhou +2 more
exaly +3 more sources
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
exaly +2 more sources
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
exaly +7 more sources
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Distributionally Robust Optimization in Possibilistic Setting
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021In this paper a class of optimization problems with uncertain constraint coefficients is discussed. Namely, for each ill-known coefficient a possibility distribution, being a membership function of a fuzzy interval, is specified. In a possibilistic interpretation, the induced possibility distribution in the set of constraint coefficient realizations ...
Romain Guillaume +2 more
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Adaptive Distributionally Robust Optimization
Management Science, 2019We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the ...
Dimitris Bertsimas +2 more
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Distributionally Robust Optimization
2021The robust optimization methodology that we have introduced so far is built on a fundamental modeling approach, that is based on set-theoretic, deterministic uncertainty models.
Xu Andy Sun, Antonio J. Conejo
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