Connectionists: Call for contributions: NIPS 2007 Workshop on Approximate Bayesian Inference in Continuous and Hybrid Models

[Matthias Seeger]

Approximate Bayesian Inference in Continuous/Hybrid Models Workshop

Neural Information Processing Systems, 7 December, 2007. Whistler, CA
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CALL FOR CONTRIBUTIONS:

http://intranet.cs.man.ac.uk/ai/nips07         abichm at gmail.com

Submission Deadline: October 26, 2007

Sponsored by the Pascal Network of Excellence and Microsoft Research
Cambridge

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Approximate inference techniques are often fundamental to machine
learning successes and this fast moving field has recently facilitated
solutions to large scale problems.

The workshop will provide a forum to discuss unsolved issues, both
practical and theoretical, pertaining to the application of
approximate Bayesian inference. The emphasis of the workshop will be
in characterizing the complexity of inference and the differential
strengths and weaknesses of available approximation techniques.
Submissions pertaining to approximate inference in both continuous and
discrete variable models are welcome.

The target audience are practitioners, providing insight into and
analysis of problems with certain methods or comparative studies of
several methods, as well as theoreticians interested in characterizing
the hardness of inference or proving relevant properties of an
established method.

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FORMAT:

The workshop will be single-day, comprising of a tutorial introduction,
invited talks (20 to 30 mins), and presentations of contributed work, with
time for discussions. Depending on quality and compatibility with workshop
aims, slots for brief talks and posters will be allocated.

We intend to have an interactive workshop, and will give priority to
contributions of novel ideas not yet established in Machine Learning,
and to critical and careful empirical comparative studies over polished
applications of established methods to standard problems.

We encourage contributions from related fields such as

* Statistics (e.g. Markov Chain Monte Carlo methods)
* Information Geometry
* Filtering, Dynamical Systems
* Computer Vision

Contributions should be communicated to the program committee (the
organizers) in form of an extended abstract (up to 8 pages in the NIPS
conference paper style), sent to abichm at gmail.com.

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Speakers:

David Wipf (UCSF)
Manfred Opper (TU Berlin)
Jason Johnson (MIT)
John Winn (MSR Cambridge)

Organizers:

Matthias Seeger   Max-Planck Biological Cybernetics, Tuebingen
David Barber      University College London
Neil Lawrence     University of Manchester
Onno Zoeter       Microsoft Research Cambridge