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2025-12-20 10:43:01
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BayesOpt 2017 BayesOpt 2017 NIPS Workshop on Bayesian Optimization December 9, 2017 Long Beach, USA Home Schedule Accepted Papers Past Workshops Special Issue Bayesian optimization for science and engineering Bayesian optimization (BO) is a recent subfield of machine learning comprising a collection of methodologies for the efficient optimization of expensive black-box functions. BO techniques work by fitting a model to black-box function data and then using the model’s predictions to decide where to collect data next, so that the optimization problem can be solved using only a small number of function evaluations. The resulting methods are characterized by their high sample-efficiency when compared to alternative black-box optimization algorithms, enabling the solution of new challenging problems. For example, in recent years, BO has become a popular tool in the machine learning community for the excellent performance attained in the problem of hyperparameter tun...
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