Results 21 to 30 of about 4,909,083 (351)
πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization [PDF]
International Conference on Learning Representations, 2022 Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms.
Carl Hvarfner +5 more
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Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction
Science and Technology of Advanced Materials: Methods, 2022 We propose a highly efficient searching algorithm in crystal structure prediction. The searching algorithm is a hybrid of the evolutionary algorithm and Bayesian optimization. The evolutionary algorithm is widely used in crystal structure prediction, and
Tomoki Yamashita +4 more
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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search [PDF]
AAAI Conference on Artificial Intelligence, 2019 Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is
Colin White +2 more
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Self-Correcting Bayesian Optimization through Bayesian Active Learning [PDF]
Neural Information Processing Systems, 2023 Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the ...
Carl Hvarfner +3 more
semanticscholar +1 more source
Co-Learning Bayesian Optimization
IEEE Transactions on Cybernetics, 2022 Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such suboptimal problem of BO can attribute to the poor surrogate accuracy of the trained Gaussian process (GP), particularly that in the ...
Zhendong Guo +3 more
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Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification
Italian National Conference on Sensors, 2023 Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves.
Ahmed M. Elshewey +5 more
semanticscholar +1 more source
Combining Bayesian optimization and Lipschitz optimization [PDF]
Machine Learning, 2020 Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization.
Mohamed Osama Ahmed +2 more
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A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization [PDF]
ACM Transactions on Evolutionary Learning and Optimization, 2021 Bayesian Optimization (BO), the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years.
M. Binois, Nathan Wycoff
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Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro, Enlu Zhou, Yifan Lin
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Bayesian Optimization for Chemical Reactions.
Chimia (Basel), 2023 Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach ...
Jeff Guo, Bojana Ranković, P. Schwaller
semanticscholar +1 more source