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CALL FOR CONTRIBUTIONS
NIPS 2012 Workshop on Probabilistic Numerics
December 7 or 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: September 16, 2012
* Notification of acceptance: October 7, 2012
* Final versions of accepted papers due: November 22, 2012
* Workshop date: December 7 or 8, 2012
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