Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems

By E. Delage, Yinyu Ye. 2010

2,113 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Certifying Some Distributional Robustness with Principled Adversarial Training (2017, background): “…robustness have considered ﬁnite-dimensional parametrizations for P , such as constraint sets for moments, support, or directional deviations [17, 20, 27], as well as non-parametric distances for probability measures such as f -divergences [5, 6, 40, 44, 21, 47], and Wasserstein distances [23,…”
Robust SAA (2014, methodology): “As mentioned, in [11, 13], the authors extended DRO to the data-driven setting.”
Distributionally robust joint chance constraints with second-order moment information (2011, methodology): “where the interchange of the maximization and minimization operations is justified by a stochastic saddle point theorem due to Shapiro and Kleywegt [26], see also Delage and Ye [11] or Natarajan et al.”
Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, background): “The main challenge lies in the inherent uncertain nature of the problem since the consumer does not know in advance when the dispatch of the provided DR resources will be requested. Zhang & Hug (2014) apply a stochastic programming approach to optimize the provision of regulation capacity by aluminu”
Robust Policy Optimization with Baseline Guarantees (2015, background): “Nevertheless, (7) is a DRO problem which in general could be NP-hard to solve [5].”
Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization (2016, methodology): “A non-exhaustive list includes moment and deviation-type constraints (Delage and Ye (2010), Goh and Sim (2010), Wiesemann et al. (2014)), Wasserstein balls (Esfahani and Kuhn (2015), Gao and Kleywegt (2016)), φ -divergence balls (Ben-Tal et al. (2013)), likelihood-based (Wang et al. (2015)) and…”
TRACTABLE ROBUST EXPECTED UTILITY AND RISK MODELS FOR PORTFOLIO OPTIMIZATION (2010, neutral): “The research was done while J.U. was a student in the Computational Engineering Programme, Singapore-MIT Alliance, National University of Singapore, Singapore 117576.”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “Therefore, various ways of constructing ambiguity sets based on uncertainty data were extensively studied [114, 116, 117].”
Theory and Applications of Robust Optimization (2010, background): “erspective. While some RO approaches build uncertainty sets directly from data, most of the models in the Robust Optimization literature are not directly connected to data. Recent work on this issue ([64], [24]) have started to lay a foundation 42 to this perspective. Further developing a data-drive”
Distributionally Robust Co-Optimization of Energy and Reserve Dispatch (2016, methodology): “DRO Distributionally Robust Optimization.”
Adaptive Distributionally Robust Optimization (2019, background): “…for instance, ˇ Z´aˇckov´a 1966, Dupacova 1987, Breton and EI Hachem 1995, Shapiro and Kleywegt 2002, Shapiro and Ahmed 2004), and recently to distributionally robust optimization models (see, for instance, Chen et al. 2007, Chen and Sim 2009, Popescu 2007, Delage and Ye 2010, Xu and Mannor 2012).”
Distributionally Robust Chance Constrained Optimal Power Flow with Renewables: A Conic Reformulation (2018, background): “Similar to [35], [43], [44], let us consider the ambiguity set defined by first and second moments as”
Robust sample average approximation (2014, methodology): “We compare its solution to SAA, 2-SAA, and the method of [15] (which in this case can also be”
Robust Optimization using Machine Learning for Uncertainty Sets (2014, methodology): “The proof is provided in Section 8.2.”
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction (2020, background): “Our work, then, is a stepping stone toward more uniform notions of validity, and we believe exploring approaches to this—perhaps by distributionally robust optimization (Delage and Ye, 2010; Duchi and Namkoong, 2018), perhaps by uniform convergence arguments (Barber et al., 2019, Sec.”
Data-driven chance constrained stochastic program (2015, methodology): “Second, we follow the procedure described in Delage and Ye [12] to construct a nonparametric confidence set for the mean and covariance matrix of ξ as follows:”
Multi-stage distributionally robust optimization with risk aversion (2021, background): “[11] proposed a single-stage distributionally robust CVaR model under moment uncertainty.”