Learning With Multiple Views

Workshop at the 22nd International Conference on Machine Learning (ICML)

Bonn, Germany, August 11th, 2005

Contact address: multiview@ls8.cs.uni-dortmund.de


Multi-view learning is a natural, yet non-standard new problem setting; it describes the problem of learning from data 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 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 extent aware of the relationships between their methods and a possible common underlying principle.
The workshop aims at bringing together researchers who are working on learning problems with multiply represented instances and consensus maximizing learning methods; our goals are to make the intrinsic structure of this field more clearly visible and to bring this interesting and rapidly developing area to the attention of additional researchers.
Topics of the workshop 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


Please note that this schedule is preliminary. In particular, starting times of the sessions may change.
9:00 - 9:25A Co-Regularization Approach to Semi-supervised learning with Multiple ViewsV. Sindwhani et al.
9:25 - 9:50Analytical Kernel Matrix Completion with Incomplete Multi-View DataD. Williams and L. Carin
9:50 - 10:15Active Learning of Features and LabelsB. Krishnapuram et al.
10:15 - 10:40Spectral Clustering with Two ViewsV. de Sa
Coffee Break
11:00 - 11:40Invited Talk: Comparability and Semantics in Mining Multi-Represented ObjectsM. Schubert
11:40 - 12:05Hybrid Hierachical Clustering: Forming a Tree From Multiple View sA. Gupta and S. Dasgupta
12:05 - 12:30Estimation of Mixture Models using Co-EMS. Bickel and T. Scheffer
Lunch Break
14:00 - 14:40Invited Talk:R. Ghani
14:40 - 15:05Using Unlabeled Texts for Named-Entity RecognitionM. Rössler and K. Morik
15:05 - 15:30Optimising Selective Sampling for Bootstrapping Named Entity RecognitionM. Becker et al.
Coffee Break
16:00 - 16:25The use of machine translation tools for cross-lingual text miningB. Fortuna and J. Shawe-Taylor
16:25 - 16:50Multiple Views in Ensembles of Nearest Neighbor ClassifiersO. Okun and H. Priisalu
16:50 - 17:15Interpreting Classifiers by Multiple ViewsS. Rüping
17:15 - 17:30Final Discussion 

The Proceedings are available online: [PDF].

Program Committee

The program committee consists of:
  • Steven Abney, University of Michigan
  • Steffen Bickel, Humboldt University
  • Ulf Brefeld, Humboldt University
  • Sanjoy Dasgupta, University of California, San Diego
  • Johannes Fürnkranz, 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 Schölkopf, Max Planck Institute for Biological Cybernetics

Important Dates

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


  • Proceedings: [PDF]
  • Call for Papers: [PS] [PDF] [TXT]
  • Workshop Proposal: [PS] [PDF] (the original workshop proposal, contains some additional information)