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