Connectionists: NIPS 2007 WORKSHOP on Topology Learning

[Canu Stephane]

========================= CALL FOR PARTICIPATION ======================

                        NIPS 2007 Workshop

*New challenges at the crossing of Machine Learning,
Computational Geometry and Topology*

                       7 or 8 December, 2007
           Whistler, British Columbia, Canada

            Supported by the PASCAL network of excellence
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E-mail: topolearn2007 at gmail.com <mailto:topolearn2007 at gmail.com>
Web site: http://topolearnnips2007.insa-rouen.fr/

Important dates
* Submission deadline : october 12, 2007
* Notification of acceptance: october 19, 2007

*Invited speakers*

* Pr. Herbert Edelsbrunner (Duke Univ., NC, USA) -
  Research on algebraic topological tools for high dimensional data 
analysis and
  the study of families of shapes. (http://www.cs.duke.edu/~edels/ 
<http://www.cs.duke.edu/%7Eedels/>)

* Pr. Partha Niyogi (Univ. of Chicago, IL, USA) - Research on Machine 
Learning
  and Information Extraction. ( http://people.cs.uchicago.edu/~niyogi/ 
<http://people.cs.uchicago.edu/%7Eniyogi/>)

* Pr. Jean-Daniel Boissonnat (INRIA Sophia Antipolis, France) - Research
  on Geometric Computing. ( 
http://www.inria.fr/personnel/Jean-Daniel.Boissonnat.en.html)

* Pr. Mathias Hein (Saarland Univ., Saarbrücken, Germany) - Research
  on semi-supervised learning and kernel-based algorithms. ( 
http://www.kyb.mpg.de/~mh <http://www.kyb.mpg.de/%7Emh>)

* Dr. Vin de Silva (Pomona College, CA, USA) - Research on Computational 
and Statistical
  Topology. ( http://pages.pomona.edu/~vds04747/public/index.html 
<http://pages.pomona.edu/%7Evds04747/public/index.html>)


*Topic
*

There is a growing interest in Machine Learning, in applying geometrical 
and topological tools to high-dimensional data analysis and processing.
Considering a finite set of points in a high-dimensional space, the 
approaches developed in the field of Topology Learning intend to learn, 
explore and exploit the topology of the shapes (topological invariants 
such as the connectedness, the intrinsic dimension or the Betti 
numbers), manifolds or not, from which these points are supposed to be 
drawn.
Applications likely to benefit from these topological characteristics 
have been identified in the field of Exploratory Data Analysis, Pattern 
Recognition, Process Control, Semi-Supervised Learning, Manifold 
Learning and Clustering.

However it appears that the integration in the Machine Learning and 
Statistics frameworks of the problems we are faced with in Topology 
Learning, is still in its infancy. So we wish this workshop to ignite 
cross-fertilization between Machine Learning, Computational Geometry and 
Topology, likely to benefit to all of them by leading to new approaches, 
deeper understanding, and stronger theoretical results about the 
problems carried by Topology Learning.
*Trends*

We wish this workshop to do the spadework on the following open problems 
and discuss the proposed solutions:
   * Theory: How and under which conditions to ensure provably correct 
topology with respect to the data? Especially facing noisy, multi-scale, 
multidimensional or incomplete datasets?
  * Algorithms: How to cope with multidimensional or massive datasets in 
reasonable memory and time? Can we provide objective criteria to tune 
the hyper-parameters?
   * Applications: How can we insert the topological knowledge into 
Machine Learning algorithms? When is it beneficial to do so? How to 
visually represent the resulting topology to the analyst in case of 
exploratory data analysis? Can we define some benchmark of real and 
artificial data specific to this field?

*Submission
*Authors are invited to submit an abstract based on original research or 
already published results, describing new methods they developed, open 
problems they are faced with or applications they tackle, fitting the 
topic and trends given above. Abstracts should not exceed 2 
single-spaced pages with figures and references. If the authors believe 
that more details are essential to substantiate the main claims of their 
abstract, they may include a clearly marked appendix that will be read 
at the discretion of the scientific committee.
Abstracts shall be sent by e-mail to: topolearn2007 at gmail.com 
<mailto:topolearn2007 at gmail.com> with subject "SUBMIT".

*Organizers*
   * Michaël Aupetit (chair), CEA-IDF, France
   * Frédéric Chazal, INRIA-Futurs, France
   * Gilles Gasso, INSA-Rouen, France
   * David Cohen-Steiner, INRIA-Sophia, France
   * Pierre Gaillard, CEA-IDF, France

*Registration*
http://nips.cc/Conferences/2007/ <mailto:topolearn2007 at gmail.com>

-- 
Stephane Canu
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LITIS - INSA de Rouen - B.P. 08 76801 St Et du Rouvray, France
+33 2 32 95 98 44 - scanu at insa-rouen.fr - asi.insa-rouen.fr/~scanu