BO 2019 - Special issue on Bayesian Optimization

Journal of Machine Learning Research (JMLR)
Special issue on Bayesian Optimization
https://bayesopt.github.io/special-issue.html
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.
IMPORTANT DATES & SUBMISSION
Deadline for manuscript submission: 31 March 2018
First Round of Reviews: May 2018
Final Decision: 01 December 2018
Publication will be in early 2019
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 (references included)
CONTACT US:
For further information you can contact us at bayesopt.jmlr-AT-gmail.com
GUEST EDITORS:
Roberto Calandra, University of California, Berkeley, United States
Roman Garnett, Washington University in St. Louis, United States
Javier González, Amazon.com, UK
Frank Hutter, University of Freiburg, Germany
Bobak Shahriari, Thoughtexchange, Canada