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!
============================================== 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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