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NIPS 2011 Workshop on "Cosmology Meets Machine Learning", December, Spain
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Sarah Bridle

Joined: 24 Sep 2004
Posts: 149
Affiliation: University College London (UCL)

PostPosted: October 18 2011  Reply with quote

We welcome cosmology submissions which use machine learning methods for posters and spotlight (short) presentations at the below.
This is the biggest machine learning conference there is, so it is very exciting to have a cosmology workshop there.
We plan to allow lots of time for interaction with the many interesting machine learners who are likely to be there and interested in our cosmology applications.
Hope to see you there,

Call for Contributions

NIPS 2011 Workshop on
"Cosmology Meets Machine Learning"
Sierra Nevada, Spain, December 16 or 17, 2011.


Submission for contributions is now open!

Join us for an exciting program including invited talks by:

* Prof. Dr. Anthony Tyson, UC Davis
* Prof. Dr. Alexandre Refregier, ETH Zurich
* Prof. Dr. Jean-Luc Starck, CEA Saclay Paris
* Prof. Dr. David Hogg, New York University

Important Dates

* November 2, 2011 Abstract submission deadline
* November 12, 2011 Notification of acceptance
* December 16 or 17, 2011 Workshop

About the Conference

NIPS is one of the leading and most important international
conferences in the field of Machine Learning, Computational Statistics
and Artificial Intelligence and enjoys a long tradition and strong
reputation. For more information please visit the meeting webpage.


Cosmology aims at the understanding of the universe and its evolution
through scientific observation and experiment and hence addresses one
of the most profound questions of human mankind. With the
establishment of robotic telescopes and wide sky surveys cosmology
already now faces the challenge of evaluating vast amount of data.

Multiple projects will image large fractions of the sky in the next
decade, for example the Dark Energy Survey will culminate in a
catalogue of 300 million objects extracted from peta-bytes of
observational data. The importance of automatic data evaluation and
analysis tools for the success of these surveys is undisputed.

Many problems in modern cosmological data analysis are tightly related
to fundamental problems in machine learning, such as classifying stars
and galaxies and cluster finding of dense galaxy populations. Other
typical problems include data reduction, probability density
estimation, how to deal with missing data and how to combine data from
different surveys.

An increasing part of modern cosmology aims at the development of new
statistical data analysis tools and the study of their behaviour and
systematics often not aware of recent developments in machine learning
and computational statistics.

Therefore, the objectives of this workshop are two-fold:

(i) The workshop aims to bring together experts from the Machine
Learning and Computational Statistics community with experts in the
field of cosmology to promote, discuss and explore the use of machine
learning techniques in data analysis problems in cosmology and to
advance the state of the art.

(ii) By presenting current approaches, their possible limitations, and
open data analysis problems in cosmology, this workshop aims to
encourage scientific exchange and to foster collaborations among the
workshop participants.

Submission Instructions

We invite submission of abstracts on topics in the following areas:

* challenging problems in cosmology data analysis
* applications of machine learning methods in cosmological data analysis problems

Submissions should not exceed 200 words and will be judged on
technical merit, the potential to generate discussion, and their
ability to foster collaboration within the workshop participants.
Accepted papers will be presented at the poster session with an
additional poster spotlight presentation. One author of every accepted
paper has to attend the workshop to present the poster and spotlight talk.

Submissions should be sent to [Log in to view email]

Organising Committee

Michael Hirsch, Universtiy College London
Sarah Bridle, University College London
Stefan Harmeling, Max Planck Institute for Intelligent Systems
Phil Marshall, Oxford University
Mark Girolami, University College London
Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems
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