Results 21 to 30 of about 56,420 (266)
Efficient Counterexample Generation for Control Systems Using Multi-Fidelity Bayesian Optimization
IEEE AccessTesting controllers in safety-critical systems is vital for ensuring their safety and preventing catastrophic failures. In this paper, we address the falsification problem within closed-loop control systems through simulation.
Zahra Shahrooei +2 more
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Bayesian Functional Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Bayesian optimization (BayesOpt) is a derivative-free approach for sequentially optimizing stochastic black-box functions. Standard BayesOpt, which has shown many successes in machine learning applications, assumes a finite dimensional domain which often is a parametric space.
Vien, Ngo Anh +2 more
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Warm starting Bayesian optimization [PDF]
2016 Winter Simulation Conference (WSC), 2016 To Appear in the Proc.
Matthias Poloczek +2 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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Bayesian Optimization with Local Search [PDF]
, 2020 Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple starting points. In this work we propose a new multi-start algorithm where the starting points are determined in a Bayesian ...
Yuzhou Gao, Tengchao Yu, Jinglai Li
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NUBO: A Transparent Python Package for Bayesian Optimization
Journal of Statistical SoftwareNUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators.
Mike Diessner +2 more
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Bayesian optimization on networks
Journal of Computational PhysicsThis paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
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Exploratory Landscape Validation for Bayesian Optimization Algorithms
MathematicsBayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
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MethodsX, 2019 Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed
Derek C. Manheim, Russell L. Detwiler
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Stratified Bayesian Optimization [PDF]
, 2018 We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong dependence to improve performance.
Saul Toscano-Palmerin, Peter I. Frazier
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