A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks. [PDF]
Transp Res Part C Emerg Technol, 2023 Xu Y, Wandelt S, Sun X.
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Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space-Air-Ground Integrated Network. [PDF]
Sensors (Basel), 2023 Li Z, Chen P.
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Distributionally robust optimization for fire station location under uncertainties. [PDF]
Sci Rep, 2022 Ming J, Richard JP, Qin R, Zhu J.
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Sensitivity analysis of Wasserstein distributionally robust optimization problems. [PDF]
Proc Math Phys Eng Sci, 2021 Bartl D, Drapeau S, Obłój J, Wiesel J.
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Wasserstein Distributionally Robust Learning
, 2020 Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is only observable through a set of training samples.
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Distributionally Robust Reinforcement Learning
, 2019 Real-world applications require RL algorithms to act safely. During learning process, it is likely that the agent executes sub-optimal actions that may lead to unsafe/poor states of the system. Exploration is particularly brittle in high-dimensional state/action space due to increased number of low-performing actions.
Smirnova, Elena +2 more
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Distributionally Robust Energy Optimization with Renewable Resource Uncertainty
MathematicsWith the increasing prevalence of intermittent power generation, the volatility, intermittency, and randomness of renewable energy pose significant challenges to the planning and operation of distribution networks.
Zhangyi Wang +5 more
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Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots. [PDF]
Inf Syst Front, 2022 Song X +5 more
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Distributionally robust mean-absolute deviation portfolio optimization using wasserstein metric. [PDF]
J Glob Optim, 2022 Chen D, Wu Y, Li J, Ding X, Chen C.
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Distributionally Robust Receive Combining
IEEE Transactions on Signal ProcessingThis article investigates signal estimation in wireless transmission (i.e., receive combining) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be
Shixiong Wang, Wei Dai, Geoffrey Ye Li
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