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Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full ...
P. Frazier
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Bayesian optimization algorithms for accelerator physics [PDF]
Physical Review Accelerators and Beams, 2023 Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations.
Ryan Roussel +26 more
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Bayesian optimization for computationally extensive probability distributions. [PDF]
PLoS ONE, 2018 An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique.
Ryo Tamura, Koji Hukushima
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The impact of Bayesian optimization on feature selection
Scientific ReportsFeature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature ...
Kaixin Yang, Long Liu, Yalu Wen
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Recent Advances in Bayesian Optimization [PDF]
ACM Computing Surveys, 2022 Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications ...
Xilu Wang +3 more
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Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces [PDF]
Neural Information Processing Systems, 2023 Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and ...
Leonard Papenmeier +2 more
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A Study of Bayesian Neural Network Surrogates for Bayesian Optimization [PDF]
International Conference on Learning Representations, 2023 Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact ...
Y. Li, Tim G. J. Rudner, A. Wilson
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An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
Scientific Reports, 2023 For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques.
Amala Mary Vincent, P. Jidesh
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Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining. [PDF]
Chem SciWalter LJ, Bereau T.
europepmc +3 more sources
Transfer Learning for Bayesian Optimization: A Survey [PDF]
arXiv.org, 2023 A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.
Tianyi Bai +5 more
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