Call for participation: ECML-PKDD ws on Learning from Non-IID Data

Call for Participation
Workshop on Learning from non-IID data: Theory, Algorithms and Practice
During ECML-PKDD 2009
7 September 2009, Bled, Slovenia

Description
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Both classification and regression frameworks in Machine Learning were developed under the independently and identically distributed (IID) assumption. Though this assumption helps to study the properties of learning procedures (e.g. generalization ability), and also guides the building of new algorithms, there are many real world situations where it does not hold. This is particularly the case for many challenging tasks of machine learning that have recently received much attention such as (but not limited to): ranking, active learning, hypothesis testing, learning with graphical models, prediction on graphs, mining (social) networks, multimedia or language processing.

This workshop is the first one that adresses specifically the problem of learning from non-IID data. The goal of the workshop is to bring together research works aiming at identifying problems where either the assumption of identical distribution or independency, or both, is violated, and where it is anticipated that carefully taking into account the non-IIDness is of primary importance.
Examples of such problems are:

– Bipartite ranking or, more generally, pairwise classification, where pairing up IID variables entails non-IIDness: if the data may still be identically distributed, they are no longer independent;

– Active learning, where labels for specific data are requested by the learner: the independence assumption is also violated;

– Learning with covariate shift, where the training and test marginal distributions of the data differ: the identically distributed assumption does not hold.

– Online learning from streaming data, when the distribution of the incoming examples changes over time: the examples are not identically distributed.

Keynote Speakers
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Shai Ben-David, University of Waterloo, Canada
Title: Towards theoretical understanding of domain adaptation learning

Nicolas Vayatis, École Normale Supérieure de Cachan, France
Title: Empirical risk minimization with statistics of higher order with examples from bipartite ranking

Workshop Program
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More information on the workshop website
http://www-connex.lip6.fr/~amini/ecml-wk-lniid.html

Program Committee
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Shai Ben-David, University of Waterloo, Canada
Gilles Blanchard, Fraunhofer FIRST (IDA), Germany
Stéphan Clémençon, Télécom ParisTech, France
François Denis, University of Provence, France
Claudio Gentile, University dell’Insubria, Italy
Balaji Krishnapuram, Siemens Medical Solutions, USA
François Laviolette, Université Laval, Canada
Xuejun Liao, Duke University, USA
Richard Nock, University Antilles-Guyane, France
Daniil Ryabko, INRIA, France
Marc Sebban, University of Saint-Etienne, France
Ingo Steinwart, Los Alamos National Labs, USA
Masashi Sugiyama, Tokyo Institute of Technology, Japan
Nicolas Vayatis, École Normale Supérieure de Cachan, France
Zhi-Hua Zhou, Nanjing University, China

Organizers
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Massih-Reza Amini, National Research Council, Canada
Amaury Habrard, University of Marseille, France
Liva Ralaivola, University of Marseille, France
Nicolas Usunier, University Pierre et Marie Curie, France

Sponsors
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-Laboratoire d’Informatique Fondamentale de Marseille (LIF)
http://www.lif.univ-mrs.fr
-PASCAL2 Network of Excellence
http://www.pascal-network.org
-ECML-PKDD 2009 organisation
http://www.ecmlpkdd2009.net