Results 1 to 10 of about 9,068 (152)
Distributionally robust learning-to-rank under the Wasserstein metric [PDF]
PLoS ONE, 2023 Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional ...
Shahabeddin Sotudian +2 more
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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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Sinkhorn Distributionally Robust Conditional Quantile Prediction with Fixed Design [PDF]
EntropyThis paper proposes a novel data-driven distributionally robust framework for conditional quantile prediction under the fixed design setting of the covariates, which we refer to as Sinkhorn distributionally robust conditional quantile prediction.
Guohui Jiang, Tiantian Mao
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Distributionally robust free energy principle for decision-making [PDF]
Nature CommunicationsDespite their groundbreaking performance, autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirable behaviors or even catastrophic failures.
Allahkaram Shafiei +3 more
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A time-coupled multi-objective distributionally robust chance-constrained framework for grid resilience enhancement using mobile emergency generators [PDF]
Scientific ReportsThis study presents a time-coupled, multi-objective distributionally robust chance-constrained (MODRCC) framework for resilient grid restoration using Mobile Emergency Generators (MEGs). The model unifies (i) time-expanded logistics for MEG routing, crew
D. Ashokaraju +4 more
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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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Shortfall-Based Wasserstein Distributionally Robust Optimization
Mathematics, 2023 In this paper, we study a distributionally robust optimization (DRO) problem with affine decision rules. In particular, we construct an ambiguity set based on a new family of Wasserstein metrics, shortfall–Wasserstein metrics, which apply normalized ...
Ruoxuan Li, Wenhua Lv, Tiantian Mao
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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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