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• W2896840177 abstract "We provide a method to solve optimization problem when objective function is a complex stochastic simulator of an urban transportation system. To reach this goal, a Bayesian optimization framework is introduced. We show how the choice of prior and inference algorithm effect the outcome of our optimization procedure. We develop dimensionality reduction techniques that allow for our optimization techniques to be applicable for real-life problems. We develop a distributed, Gaussian Process Bayesian regression and active learning models that allow parallel execution of our algorithms and enable usage of high performance computing. We present a fully Bayesian approach that is more sample efficient and reduces computational budget. Our framework is supported by theoretical analysis and an empirical study. We demonstrate our framework on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Finally, we discuss directions for further research." @default.
• W2896840177 created "2018-10-26" @default.
• W2896840177 creator A5012467860 @default.
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• W2896840177 date "2018-10-08" @default.
• W2896840177 modified "2023-09-27" @default.
• W2896840177 title "Practical Bayesian Optimization for Transportation Simulators" @default.
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• W2896840177 hasPublicationYear "2018" @default.
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