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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 ...
Bart Van Parys +2 more
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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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Distributionally robust modeling of optimal control
Operations Research Letters, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Distributionally Robust Optimization under Distorted Expectations
SSRN Electronic Journal, 2020Optimal Decision Making Under Distorted Expectation with Partial Distribution Information Decision makers who are not risk neutral may evaluate expected values by distorting objective probabilities to reflect their risk attitudes, a phenomenon known as distorted expectations. This concept is widely applied in behavioral economics, insurance, finance,
Jun Cai +2 more
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Dropout training is distributionally robust optimal
J. Mach. Learn. Res., 2023Summary: This paper shows that dropout training in generalized linear models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model.
Jose H. Blanchet +4 more
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An Approximation Scheme for Distributionally Robust Nonlinear Optimization
SIAM Journal on Optimization, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Johannes Milz, Michael Ulbrich
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Distributionally Robust Stochastic Optimization with Wasserstein Distance
Mathematics of Operations Research, 2023Rui Gao, Anton J Kleywegt
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