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 …
A tutorial on Bayesian optimization
PI Frazier - arxiv preprint arxiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
Roadmap on machine learning in electronic structure
In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
Bayesian optimization for adaptive experimental design: A review
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …
“black-box” functions. This review considers the application of Bayesian optimisation to …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
A Bayesian experimental autonomous researcher for mechanical design
While additive manufacturing (AM) has facilitated the production of complex structures, it has
also highlighted the immense challenge inherent in identifying the optimum AM structure for …
also highlighted the immense challenge inherent in identifying the optimum AM structure for …
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
Bayesian optimization (BO) has been leveraged for guiding autonomous and high-
throughput experiments in materials science. However, few have evaluated the efficiency of …
throughput experiments in materials science. However, few have evaluated the efficiency of …