Results 1 to 10 of about 9,044 (253)
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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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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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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Journal of Modern Power Systems and Clean Energy, 2022 Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable ...
Jingwei Hu +3 more
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Journal of Modern Power Systems and Clean Energy, 2023 Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units
Xiaodong Zheng +6 more
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AIMS Mathematics, 2023 Robust optimization is a new modeling method to study uncertain optimization problems, which is to find a solution with good performance for all implementations of uncertain input.
Zhimin Liu
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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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Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets [PDF]
, 2018 We present a data-driven approach for distributionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty.
Cherukuri, Ashish +2 more
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A distributionally robust perspective on uncertainty quantification and chance constrained programming [PDF]
, 2015 The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability.
Hanasusanto, GA +3 more
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