Results 11 to 20 of about 2,032 (302)
Kernel Distributionally Robust Optimization [PDF]
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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Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space–Air–Ground Integrated Network [PDF]
Sensors, 2023 As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ...
Zhiyuan Li, Pinrun Chen
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Regularization for Wasserstein distributionally robust optimization
ESAIM: Control, Optimisation and Calculus of Variations, 2023 Optimal transport has recently proved to be a useful tool in various machine learning applications needing comparisons of probability measures. Among these, applications of distributionally robust optimization naturally involve Wasserstein distances in their models of uncertainty, capturing data shifts or worst-case scenarios.
Azizian, Waïss +2 more
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Distributionally robust optimization for fire station location under uncertainties [PDF]
Scientific Reports, 2022 Emergency fire service (EFS) systems provide rescue operations for emergencies and accidents. If properly designed, they can decrease property loss and mortality.
Jinke Ming +3 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 Bayesian Quadrature Optimization
CoRR, 2020 AISTATS2020
Thanh Tang Nguyen +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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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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