We are excited to announce, in collaboration with the Journal of Machine Learning Research (JMLR), a special issue on Bayesian Optimization. We encourage all interested researchers to submit their manuscripts for consideration.
Journal of Machine Learning Research (JMLR)
Special issue on Bayesian Optimization
Bayesian optimization has emerged as an exciting subfield of machine learning that is concerned with optimization using probabilistic methods. Systems implementing Bayesian optimization techniques have been successfully used to solve difficult problems in a diverse set of applications, including automatic tuning of ML algorithms, robotics, and many other systems. Several 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.
We welcome any submissions related to Bayesian optimization, including contributions to modeling, methodology, extensions, practical issues, and applications.
NOTE: Due to an overwhelming number of submissions, finding appropriate high-quality reviewers proved difficult. Most reviews should be out by August on a rolling base.
Manuscripts should be prepared according to the JMLR submission procedure (http://www.jmlr.org/author-info.html#Submission), and the submission should be done via the JMLR electronic submission management system (http://jmlr.csail.mit.edu/manudb/) by selecting “bayesopt” as special issue.
All submissions should be limited to a maximum of 24 pages (Including reference, appendix, and any other supplementary material).
For further information you can contact us at firstname.lastname@example.org