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CALL FOR CONTRIBUTIONS
NIPS 2012 Workshop on Probabilistic Numerics
December 8, 2012 at Lake Tahoe, Nevada, US
http://www.probabilistic-numerics.org
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Overview:
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Traditionally, machine learning uses numerical algorithms as tools.
But many tasks in numerics can also be interpreted as learning problems.
Some examples:

* How can optimizers model the objective function, and how should
they use the model to act?

* How should a quadrature method use observations of the integrand to
estimate the integral, and at which points should it collect them?

* Can approximate inference techniques be applied to numerical problems?

Many such issues can be seen as special cases of decision theory,
active learning, or reinforcement learning, but numerical tasks
present exceptional demands on computational cost and robustness, so
standard methods from these fields require modification to be useful.

We invite contribution of recent results in the development and
interpretation of numerical analysis methods based on probability theory.
This includes, but is not limited to the areas of optimization, sampling,
linear algebra, quadrature and the solution of differential equations.

Submission instructions are available at

http://www.probabilistic-numerics.org/Call.html

Invited Speakers (confirmed):
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Persi Diaconis, Stanford University
Matthias Seeger, Ecole Polytechnique Fédérale de Lausanne
Mark Girolami, University College London

Organizers:
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Philipp Hennig, Max Planck Society, Tübingen
Michael Osborne, University of Oxford
John Cunningham, Washington University in St. Louis

Important Dates:
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* Submission of extended abstracts: November 2, 2012
* Notification of acceptance: November 23, 2012
* Final versions of accepted papers due: December 1, 2012
* Workshop date: December 8, 2012