Connectionists: [CFP] NIPS 2016 workshop on Bayesian optimization -- Submission deadline: October 16

[Bobak Shahriari]

Hello everyone,

Apologies for inevitable cross-postings. We are pleased to announce another
installment of the Bayesian optimization workshop! This year our theme is
Black-box optimization and Beyond (see below for a description). Please
visit http://bayesopt.com/ for more details. Hope to see you there!

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Call for Papers
Bayesian Optimization: Black-box Optimization and Beyond
Date: December 10, 2016
Location: Barcelona, Spain (part of the NIPS 2016 workshops)
Submission Deadline: *October 16, 2016*
Website: http://bayesopt.com/
==============================================

### Important dates:
Submission deadline: 16 October (11:59 pm PST)
Author notification: 2 November
Camera-ready: 4 December

### Abstract:
Classically, Bayesian optimization has been used purely for expensive
single-objective black-box optimization. However, with the increased
complexity of tasks and applications, this paradigm is proving to be too
restricted. Hence, this year’s theme for the workshop will be “black-box
optimization and beyond”. Among the recent trends that push beyond BO we
can briefly enumerate: - Adapting BO to not-so-expensive evaluations. -
“Open the black-box” and move away from viewing the model as a way of
simply fitting a response surface, and towards modelling for the purpose of
discovering and understanding the underlying process. For instance, this
so-called grey-box modelling approach could be valuable in robotic
applications for optimizing the controller, while simultaneously providing
insight into the mechanical properties of the robotic system. -
“Meta-learning”, where a higher level of learning is used on top of BO in
order to control the optimization process and make it more efficient.
Examples of such meta-learning include learning curve prediction,
Freeze-thaw Bayesian optimization, online batch selection, multi-task and
multi-fidelity learning. - Multi-objective optimization where not a single
objective, but multiple conflicting objectives are considered (e.g.,
prediction accuracy vs training time).

### Invited speakers and panelists:
- Joshua Knowles (University of Birmingham)
- Jasper Snoek (Twitter)
- Marc Toussaint (University of Stuttgart)
- Roman Garnett (Washington University in St. Louis)
- Will Welch (University of British Columbia)
- Katharina Eggensperger (University of Freiburg)

### Organizers:
- Roberto Calandra (UC Berkeley)
- Bobak Shahriari (University of British Columbia)
- Javier Gonzalez (Amazon)
- Frank Hutter (University of Freiburg)
- Ryan P. Adams (Harvard University)

Looking forward to seeing many of you in Barcelona!

Roberto, Bobak, Javier, Frank, and Ryan
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