Results 11 to 20 of about 9,044 (253)
Distributionally Robust Portfolio Optimization. [PDF]
Proc IEEE Conf Decis Control, 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 ...
Bardakci IE, Lagoa CM.
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Distributionally robust profit opportunities [PDF]
Operations Research Letters, 2021 arXiv admin note: text overlap with arXiv:2004 ...
Derek Singh, Shuzhong Zhang
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Distributionally Robust Learning [PDF]
Foundations and Trends® in Optimization, 2020 This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning with fundamental properties of the Wasserstein metric and the DRO formulation, we explore duality to arrive at tractable formulations
Ioannis Ch. Paschalidis, Ruidi Chen
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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, Enlu Zhou, Yifan Lin
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Distributionally Robust Mechanism Design [PDF]
Management Science, 2017 We study a mechanism design problem in which an indivisible good is auctioned to multiple bidders for each of whom it has a private value that is unknown to the seller and the other bidders. The agents perceive the ensemble of all bidder values as a random vector governed by an ambiguous probability distribution, which belongs to a commonly known ...
Çağıl Koçyiğit +3 more
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Distributionally Robust and Generalizable Inference
Statistical Science, 2023 We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is not drawn i.i.d. from the target population. For example, unobserved sampling bias, batch effects, or unknown associations might inflate the variance compared to i.i.d ...
Rothenhäusler, Dominik +1 more
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Journal of Modern Power Systems and Clean Energy, 2019 This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty.
Xin Fang +4 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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Distributionally Robust Domain Adaptation
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples ...
Awad, Akram S., Atia, George K.
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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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