[Amdnl] RV: [CfP:ALED@ IJCNN13] Active Learning and Experimental Design (Special Session at IJCNN 2013)

[Jose Garcia]

[sorry for cross posting
]

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Dear Colleague,

We would like to cordially invite you to submit a paper for the special
session on

Active Learning and Experimental Design

Special Session at IJCNN 2013

[link <http://perso.rd.francetelecom.fr/lemaire/IJCNN2013/> 
]

 

Fairmont Hotel • Dallas, TX

August 4–9, 2013

 <http://www.ijcnn2013.org/> http://www.ijcnn2013.org/


organized within International Joint Conference on Neural Networks sponsored
jointly by INNS and the IEEE Computational Intelligence Society.

This special session offers a meeting opportunity for academics and industry
researchers belonging to the communities of Computational Intelligence,
Machine Learning, Experimental Design, Causal Discovery, and Data Mining to
discuss new areas of active learning and experimental design, and to bridge
the gap between data acquisition or experimentation and model building. The
focus is on how active sampling and data acquisition should contribute to
the design and modeling of highly intelligent learning systems.

Machine learning prescribes methods and algorithms, which allow a model to
learn a behavior from examples. Active learning gathers methods, which
select subsets of examples or variables to be used to build a training set
for the predictive model. Strategies must be devised to select a subset of
examples and variables as small and informative as possible for a task at
hand. As a special case, we consider the problem of causal discovery in
which one must uncover variables susceptible of influencing a target of
interest quantitatively, due to a cause-effect relationship, and check such
hypothesis experimentally. Research on incremental experimental design is
particularly relevant to this call. 

When designing active learning algorithms for real-world data, some specific
issues are raised. The main ones are scalability and practicability. Methods
must be able to handle high volumes of data, in spaces of possibly
high-dimension, and the process for labeling new examples by an expert must
be optimized. This includes making "de novo" queries or equivalently for
causal systems "manipulating" given variables. 

Publication opportunities: Papers should be submitted to IJCNN. We encourage
papers that describe applications of active learning in real-world. In the
industrial context, the main difficulties met and the original solutions
developed, have to be described. Authors of papers accepted in the ECML-ALRA
workshop (which do not have any "referenced" proceedings) are also
encouraged submit a long version of their paper (up to the maximum number of
pages at IJCNN). We are also planning a special topic of JMLR on the theme
of experimental design to uncover causal relationships, which will be
announced shortly.


Submission procedures:
<http://www.ijcnn2013.org/paper-submission.php#content>
http://www.ijcnn2013.org/paper-submission.php#content
(Important - Submission Guidelines:  Please follow the regular submission
guidelines of IJCNN 2013 and submit your paper to the paper submission
system. Be careful to select the correct special session. After your
submission notify the chairs of your submission by sending email to:
vincent.lemaire at orange.com.)


Further information: <http://www.ijcnn2013.org> http://www.ijcnn2013.org

Important Dates:
Paper Submission Deadline February 22, 2013
Camera-Ready Paper Submission May 1, 2013

Organizers
• Vincent Lemaire (Orange Labs, France)
• José García-Rodríguez (University of Alicante, Spain)
• Isabelle Guyon (Clopinet Enterprises, USA)
• Alexis Bondu (EDF, France)


Contact
vincent.lemaire at orange.com, jgarcia at dtic.ua.es, guyon at clopinet.com,
alexis.bondu at edf.fr

 

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