Bayesopt.github.io Website Review

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 tuning, with important results both in academia and industry. This success has made BO a crucial player in the current trend of “automatic machine As new BO methods have been developed, the area of applicability has been continuously expanding. While the problem of hyperparameter tuning permeates all disciplines, the field has moved towards more specific problems in science and engineering requiring of new advanced methodology. Today, Bayesian optimization is the most promising approach for accelerating and automating science and engineering. Therefore, we have chosen this year’s theme for the workshop to be “Bayesian optimization for science and engineering”. We enumerate below a few of the recent directions in which BO methodology is being pushed forward to address specific problems in Beyond Gaussian processes. While GPs are the default choice in BO, specific problems in science and engineering require to collect larger amounts of complex data. In these settings, it is necessary to use alternative models to capture complex patterns with higher accuracy. For example, Bayesian neural networks or de*p Gaussian processes. How can we do efficient BO with these type of models? Optimization in structured domains. Many problems in science and engineering require to perform optimization in complex spaces which