Connectionists: CFP: NIPS 2013 Workshop on Bayesian Optimization in Theory and Practice

[Matt Hoffman]

[Apologies for cross-postings]

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CALL FOR PAPERS

NIPS 2013 Workshop: Bayesian Optimization in Theory and Practice
Lake Tahoe, Nevada, USA, 10 December, 2013
Web: www.bayesianoptimization.org
email: nips2013.bayesopt at gmail.com

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Important Dates:
  - Submission deadline: 18 October, 2013
  - Notification of acceptance: 1 November, 2013

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Workshop Overview:

There have been many recent advances in the development of machine learning
approaches for active decision making and optimization. These advances have
occurred in seemingly disparate communities, each referring to the problem
using different terminology: Bayesian optimization, experimental design,
bandits, active sensing, automatic algorithm configuration, personalized
recommender systems, etc. Recently, significant progress has been made in
improving the methodologies used to solve high-dimensional problems and
applying these techniques to challenging optimization tasks with limited
and noisy feedback. This progress is particularly apparent in areas that
seek to automate machine learning algorithms and website analytics.
Applying these approaches to increasingly harder problems has also revealed
new challenges and opened up many interesting research directions both in
developing theory and in practical application.

Following on last year’s NIPS workshop, “Bayesian Optimization & Decision
Making”, the goal of this workshop is to bring together researchers and
practitioners from these diverse subject areas to facilitate
cross-fertilization by discussing challenges, findings, and sharing data.
This year we plan to focus on the intersection of “Theory and Practice”.
Specifically, we would like to carefully examine the types of problems
where Bayesian optimization performs well and ask what theoretical
guarantees can be made to explain this performance? Where is the theory
lacking? What are the most pressing challenges? In what way can this
empirical performance be used to guide the development of new theory?

To this end, we welcome contributions on theoretical models, empirical
studies, and applications of the above. We also welcome challenge papers on
possible applications or datasets. Topics of interest (though not
exhaustive) include:
  - Bayesian optimization
  - Sequential experimental design, bandits, Thompson sampling
  - Applications, e.g., automatic parameter tuning, active sensing, robotics
  - Related areas: active learning, reinforcement learning, etc.

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We have a number of confirmed speakers including:
  - Ryan Adams, Harvard University
  - Sebastien Bubeck, Princeton University
  - Philipp Hennig, MPI Tübingen

and the workshop will also host a panel discussion with additional
panelists including:
  - James Bergstra, University of Waterloo
  - Andreas Krause, ETH Zurich
  - Remi Munos, INRIA Lille

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Submission instructions:

Submissions should be in the NIPS 2013 format, with a maximum of 4 pages
(excluding references). Accepted papers will be made available online at
the workshop website, but the workshop proceedings can be considered
non-archival. Submissions need not be anonymous. For detailed submission
instructions, please refer to the workshop website.

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Organizers:

  - Matthew Hoffman, University of Cambridge
  - Jasper Snoek, University of Toronto,
  - Nando de Freitas, Oxford University
  - Michael Osborne, Oxford University
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