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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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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 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
Ruidi Chen, Ioannis Ch. Paschalidis
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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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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 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 ...
Akram S. Awad, George K. Atia
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Distributionally Robust Transfer Learning
CoRR, 2023 Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples.
Xin Xiong, Zijian Guo 0003, Tianxi Cai
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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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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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Distributionally Robust Selection of the Best [PDF]
Management Science, 2020 Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the
Weiwei Fan +2 more
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