Results 21 to 30 of about 3,143,268 (283)
Distributionally Robust Bayesian Optimization
CoRR, 2020 Accepted at AISTATS ...
Kirschner, Johannes +3 more
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On distributionally robust multiperiod stochastic optimization [PDF]
Computational Management Science, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Analui, B., Pflug, G.C.
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IEEE Access, 2021 Since optimal portfolio strategy depends heavily on the distribution of uncertain returns, this article proposes a new method for the portfolio optimization problem with respect to distribution uncertainty.
Ningning Du, Yankui Liu, Ying Liu
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Energy Reports, 2023 This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately.
Peishuai Li +4 more
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Distributionally Robust Portfolio Optimization
2019 IEEE 58th Conference on Decision and Control (CDC), 2019 In this paper we consider the problem of portfolio optimization involving uncertainty in the probability distribution of the assets returns. Starting with an estimate of the mean and covariance matrix of the returns of the assets, we define a class of admissible distributions for the returns and show that optimizing the worst-case risk of loss can be ...
I. E. Bardakci, Constantino M. Lagoa
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Distributionally robust optimization
Acta NumericaDistributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical ...
Daniel Kuhn 0001 +2 more
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Distributionally Robust Optimization with Probabilistic Group
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promise, they often assume that each sample belongs to one and only one group, which does not allow ...
Soumya Suvra Ghosal, Yixuan Li 0001
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The Distributionally Robust Optimization Reformulation for Stochastic Complementarity Problems
Abstract and Applied Analysis, 2014 We investigate the stochastic linear complementarity problem affinely affected by the uncertain parameters. Assuming that we have only limited information about the uncertain parameters, such as the first two moments or the first two moments as well as ...
Liyan Xu, Bo Yu, Wei Liu
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Mathematical Foundations of Distributionally Robust Multistage Optimization [PDF]
SIAM Journal on Optimization, 2021 Distributionally robust optimization involves various probability measures in its problem formulation. They can be bundled to constitute a risk functional. For this equivalence, risk functionals constitute a fundamental building block in distributionally robust stochastic programming.
Alois Pichler, Alexander Shapiro 0001
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Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems
Energies, 2018 Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity.
Mohammad Seydali Seyf Abad +3 more
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