Call for papers The NIPS Workshop on Bayesian Optimization is calling for contributions on theoretical models, empirical studies, and applications of Bayesian optimization. We also welcome challenge papers on possible applications or datasets. Topics of interest (though not exhaustive) include:
Bayesian optimization Sequential experimental design and bandits Applications, e.g., automatic parameter tuning, active sensing, robotics Related areas: active learning, reinforcement learning, etc. Invited speakers and panelists Nando de Freitas (Oxford) Steve Scott (Google) Daniel Russo (Stanford) Xavier Amatriain (Netflix) Julien Cornebise (DeepMind) Robert Gramacy (University of Chicago) Organizers Ryan P. Adams (Harvard University) Zoubin Ghahramani (University of Cambridge) Matthew W. Hoffman (University of Cambridge) Jasper Snoek (Harvard University) Kevin Swersky (University of Toronto) See also the workshop overview for more details. http://bayesianoptimization.org Submission instructions Papers must be in the latest NIPS format, but with a maximum of 4 pages (excluding references). Papers can be either anonymized or not (i.e. you can decide whether to uncomment or add \nipsfinalcopy to your document prior to submitting). The reviewing process will be anonymous. Accepted papers will also be made available on the workshop website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences. Paper submissions will be made through CMT. https://cmt.research.microsoft.com/BO2014 _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
