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NIPS BayesOpt 2013 : NIPS 2013 Workshop on Bayesian Optimization in Theory and Practice


When Dec 10, 2013 - Dec 10, 2013
Where Lake Tahoe, Nevada, USA
Submission Deadline Oct 18, 2013
Notification Due Nov 1, 2013
Final Version Due Dec 3, 2013
Categories    machine learning   optimization   applications   artificial intelligence

Call For Papers


NIPS 2013 Workshop: Bayesian Optimization in Theory and Practice
Lake Tahoe, Nevada, USA, 10 December 2013 (TBD)

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

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.

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

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.


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

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