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              CALL FOR PAPERS: LEARNING WITH MULTIPLE VIEWS

Workshop at the International Conference on Machine Learning (ICML 2005)

             http://www-ai.cs.uni-dortmund.de/MULTIVIEW2005/

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TOPIC AND GOALS OF THE WORKSHOP

Multi-view learning is a natural, yet non-standard new problem setting;
it describes the problem of learning from data where observations are
represented by multiple independent sets of features. A typical example
is learning to classify web pages by either the words on the page or the
words contained in anchor texts of links to the page.

  Multi-view learning methods have been studied by Yarowsky (1995) and
Blum and Mitchell (1998), who noticed that having multiple 
representations of examples can improve classification performance when
in addition to labeled examples, many unlabeled examples are available. 
A recent result by Abney (2002) suggests that there may be an
underlying principle which gives rise to a family of new methods: The
disagreement rate of two independent hypotheses upper-bounds the error
rate of either hypothesis.  By minimizing the disagreement rate on
unlabeled data, the error rate can be minimized.

  In the last 2-3 years, several new supervised and unsupervised methods
have been proposed which appear to utilize this consensus maximization
principle in one way or another. However, in many cases the contributors
are not to the full extend aware of the relationships between their 
methods and a possible common underlying principle. The workshop aims at
bringing together researchers working on learning problems with multiply
represented instances and consensus maximizing methods; our goals are to
make the intrinsic structure of this field more clearly visible and to 
bring this interesting area to the attention of additional researchers.


SUBMISSION

Participants should submit a 6 page paper in PDF format, following the
format guidelines of the main ICML conference. Papers must be submitted
until April 1st via the workshop website.

Possible Topics Include: 
  - analysis of algorithms: co-training, co-EM, co-EMT ...
  - active and semi-supervised learning
  - multi-view clustering and classification
  - novel learning tasks (interpretability, constraints, ...)
  - independence of views: quantification and relevance
  - text, web, and other applications
  - hierarchical, partitioning, spatial, spectral clustering
  - theoretical analyses
  - relation to other fields of learning (e.g., boosting)
  - consensus maximization principle
  - generative and discriminative models


IMPORTANT DATES

Apr  1, 2005  Paper submission deadline
Apr 22, 2005  Notification of acceptance
May 13, 2005  Final paper deadline
Aug 11, 2005  Workshop held


ORGANIZATION

Co-Chairs: 
  - Stefan R?ping, University of Dortmund
  - Tobias Scheffer, Humboldt University, Berlin

Program Committee:		     
  - Steven Abney, University of Michigan
  - Steffen Bickel, Humboldt University
  - Ulf Brefeld, Humboldt University
  - Sanjoy Dasgupta, UCSD
  - Johannes F?rnkranz, Darmstadt University
  - Rayid Ghani, Accenture
  - Thomas Hofmann, Brown University
  - Thorsten Joachims, Cornell University
  - Kristian Kersting, Freiburg University
  - Stan Matwin, University of Ottawa
  - Tom Mitchell, Carnegie Mellon University
  - Ion Muslea, SRI
  - Bernhard Sch?lkopf, MPI T?bingen