Bayesian optimization has emerged as an exciting subfield of machine learning that is concerned with the global optimization of expensive, noisy, black-box functions using probabilistic methods. Systems implementing Bayesian optimization techniques have been successfully used to solve difficult problems in a diverse set of applications. Many recent advances in the methodologies and theory underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behaviour of these algorithms. Bayesian optimization is now increasingly being used in industrial settings, providing new and interesting challenges that require new algorithms and theoretical insights.
Classically, Bayesian optimization has been used purely for expensive single-objective black-box optimization. However, with the increased complexity of tasks and applications, this paradigm is proving to be too restricted. Hence, this year’s theme for the workshop will be “black-box optimization and beyond”. Among the recent trends that push beyond BO we can briefly enumerate:
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 probabilistic optimization. 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.
A further goal of this workshop is to encourage collaboration between the diverse set of researchers involved in Bayesian optimization. This includes not only interchange between industrial and academic researchers, but also between the many different subfields of machine learning which make use of Bayesian optimization or its components. We are also reaching out to the wider optimization and engineering communities for involvement.
** Due to numerous requests the submission deadline has been postponed of one week.**