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Recent advances in Bayesian optimization
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 …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
[SÁCH][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
A survey on multi-objective hyperparameter optimization algorithms for machine learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …
performance of Machine Learning (ML) algorithms. Several methods have been developed …
Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
Constrained Bayesian optimization with noisy experiments
Constrained Bayesian Optimization with Noisy Experiments Page 1 Bayesian Analysis (2019)
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to
different CFD (computational fluid dynamics) problems which can be of practical relevance …
different CFD (computational fluid dynamics) problems which can be of practical relevance …
Replication or exploration? Sequential design for stochastic simulation experiments
We investigate the merits of replication, and provide methods for optimal design (including
replicates), with the goal of obtaining globally accurate emulation of noisy computer …
replicates), with the goal of obtaining globally accurate emulation of noisy computer …
A survey on kriging-based infill algorithms for multiobjective simulation optimization
This article surveys the most relevant kriging-based infill algorithms for multiobjective
simulation optimization. These algorithms perform a sequential search of so-called infill …
simulation optimization. These algorithms perform a sequential search of so-called infill …
Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-
driven models that would enable the shrinking horizon nonlinear model predictive control of …
driven models that would enable the shrinking horizon nonlinear model predictive control of …