Results 11 to 20 of about 6,626 (272)
Frameworks and Results in Distributionally Robust Optimization
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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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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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 ...
Wolfram Wiesemann +2 more
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Consensus Distributionally Robust Optimization With Phi-Divergence
IEEE Access, 2021 We study an efficient algorithm to solve the distributionally robust optimization (DRO) problem, which has recently attracted attention as a new paradigm for decision making in uncertain situations.
Shunichi Ohmori
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Kernel Distributionally Robust Optimization
CoRR, 2020 We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel Hilbert spaces (RKHS) to construct a wide range of convex ambiguity sets, which can be generalized to sets based on integral probability metrics and finite-order moment bounds.
Zhu, Jia-Jie +3 more
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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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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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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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Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation [PDF]
Dianli jianshe[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization
LI Jiawei, SUN Qinghe, WANG Qiong, YE Yujian, HU Heng, ZHANG Xi
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Distributionally Robust Bayesian Optimization
CoRR, 2020 Accepted at AISTATS ...
Kirschner, Johannes +3 more
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