Results 11 to 20 of about 8,860,352 (207)
Mathematical programming, 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
core +2 more sources
Distributionally Robust Feature Selection
arXiv.orgAccepted at NeurIPS ...
Swaroop, Maitreyi +2 more
openaire +3 more sources
Distributionally Robust Optimization and Robust Statistics
Statistical ScienceWe review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss between the training and deployment environments. Many well-known estimators in statistics and machine learning (e.g.
Blanchet, Jose +3 more
openaire +3 more sources
Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic. [PDF]
Comput Ind Eng, 2022 This paper presents a multi-period multi-objective distributionally robust optimization framework for enhancing the resilience of personal protective equipment (PPE) supply chains against disruptions caused by pandemics.
Ash C +3 more
europepmc +2 more sources
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response [PDF]
Applied Energy, 2023 A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution.
Yang Li +4 more
semanticscholar +1 more source
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity [PDF]
Journal of machine learning research, 2022 This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration.
Laixi Shi, Yuejie Chi
semanticscholar +1 more source
Protection and Control of Modern Power Systems, 2023 A coordinated scheduling model based on two-stage distributionally robust optimization (TSDRO) is proposed for integrated energy systems (IESs) with electricity-hydrogen hybrid energy storage.
Yibin Qiu +6 more
semanticscholar +1 more source
Learning to Solve Routing Problems via Distributionally Robust Optimization [PDF]
AAAI Conference on Artificial Intelligence, 2022 Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group
Yuan Jiang +3 more
semanticscholar +1 more source
Distributionally Robust Optimization: A review on theory and applications
Numerical Algebra, Control and Optimization, 2022 In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets ...
Fengming Lin, X. Fang, Zheming Gao
semanticscholar +1 more source
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution [PDF]
International Conference on Machine Learning, 2022 Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay approach could gradually become less effective for long data streams, as ...
Zhenyi Wang +5 more
semanticscholar +1 more source