Results 21 to 30 of about 56,129 (265)
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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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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Warm starting Bayesian optimization [PDF]
2016 Winter Simulation Conference (WSC), 2016 To Appear in the Proc.
Matthias Poloczek +2 more
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Triangulation Candidates for Bayesian Optimization
Advances in Neural Information Processing Systems 35, 2022 Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers. In such cases it is common to replace continuous search with a discrete one over random candidates.
Robert B. Gramacy +2 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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SLAS TechnologyBayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data,
Yujin Taguchi +5 more
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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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On Batch Bayesian Optimization
CoRR, 2019 All of Bayesian Nonparametrics workshop, Neural Information Processing Systems ...
Sayak Ray Chowdhury, Aditya Gopalan
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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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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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