BayesOpt 2017

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, in industry or academia, other scientific disciplines welcome
• Related areas, e.g., active learning, reinforcement learning

See also the workshop overview for more details.

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 (i.e., blind).

Accepted papers and eventual supplementary material will also be made available on the workshop website. The camera-ready papers have to include the \nipsfinalcopy. 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 conference.

Submissions can be made through

cmt.research.microsoft.com/BAYESOPT2017/.

Registration to NIPS

Presenters of accepted posters will have access to a reserved pool of NIPS tickets which includes full conference registration.

Selected posters will also be eligible for free workshop registration.

Important dates

• Submission deadline: 3 November 2017 (11:59 pm Pacific).
• Notification: 17 November 2017.
• Camera ready: 1 December 2017.
• Workshop: 9 December 2017.