Results 31 to 40 of about 9,044 (253)
Distributionally Robust Games with Risk-averse Players
, 2016 We present a new model of incomplete information games without private information in which the players use a distributionally robust optimization approach to cope with the payoff uncertainty.
Loizou, Nicolas
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Distributionally robust optimization
Acta NumericaDistributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical ...
Daniel Kuhn +2 more
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Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools, 2017 In this paper we introduce the novel framework of distributionally robust games. These are multi-player games where each player models the state of nature using a worst-case distribution, also called adversarial distribution. Thus each player's payoff depends on the other players' decisions and on the decision of a virtual player (nature) who selects ...
Bauso, Dario +2 more
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Diance yu yibiaoIn order to adapt to the uncertainty of new energy, increase new energy consumption and reduce carbon emissions, an optimal scheduling model of integrated energy distribution network system is proposed based on vine Copula considering three uncertainties
YANG Mingjie +5 more
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Energy Reports, 2021 Nowadays, the high penetration of renewable energy, with variable and unpredictable nature, poses major challenges to operation and planning studies of power systems.
Jian Le +3 more
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Energy Reports, 2023 To avoid the problem of insufficient flexibility of the power grid along the cz railway due to source-load fluctuations, a distributionally robust optimization method considering flexibility is proposed in this paper.
Jiawei Liu +5 more
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, 2017 We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability ...
Esfahani, Peyman Mohajerin, Kuhn, Daniel
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Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [PDF]
, 2020 Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available.
Anandkumar, Anima +5 more
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Distributionally Robust Language Modeling [PDF]
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019 Language models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., restaurant reviews). In this paper, we first show that training on text outside the test distribution can degrade test performance when using standard maximum ...
Oren, Yonatan +3 more
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Mathematics, 2022 Empirical evidence suggests that financial risk has a heavy-tailed profile. Motivated by recent advances in the generalized quantile risk measure, we propose the tail value-at-risk (TVaR)-based expectile, which can capture the tail risk compared with the
Haoyu Chen, Kun Fan
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