BayesOpt 2017

NIPS Workshop on Bayesian Optimization
December 9, 2017
Long Beach, USA

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 tuning, with important results both in academia and industry. This success has made BO a crucial player in the current trend of “automatic machine learning”.

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 science and engineering:

The target audience for this workshop consists of both industrial and academic practitioners of Bayesian optimization as well as researchers working on theoretical and practical advances in model based optimization across different engineering areas. We expect that this pairing of theoretical and applied knowledge will lead to an interesting exchange of ideas and stimulate an open discussion about the long term goals and challenges of the Bayesian optimization community.

The main goal of the workshop is to serve as a forum of discussion and to encourage collaboration between the diverse set of scientist that develop and use Bayesian optimization and related techniques. Researchers and practitioners in Academia are welcome, as well people form the wider optimization, engineering and probabilistic modeling communities.


Invited speakers and panelists

Panel moderator:


Important dates