Connectionists: call for abstracts - NIPS workshop on learning to campare examples

[Samy Bengio]

CALL FOR PAPERS:

NIPS'06 Workshop on Learning to Compare Examples
                     ----------------------------

                       http://www.idiap.ch/lce
                       Whistler, Canada
                       December 8-9, 2006
                       (submission deadline: October 26, 2006)

Overview:

     The identification of an effective function to compare
     examples is essential to several machine learning
     problems. For instance, retrieval systems entirely depend
     on such a function to rank the documents with respect to
     their estimated similarity to the submitted query.
     Another example is kernel-based algorithms which heavily
     rely on the choice of an appropriate kernel function. In
     most cases, the choice of the comparison function (also
     called, depending on the context and its mathematical
     properties, distance metric, similarity measure, kernel
     function or matching measure) is done a-priori, relying
     on some knowledge/assumptions specific to the task. An
     alternative to this a-priori selection is to learn a
     suitable function relying on a set of examples and some
     of its desired properties. This workshop is aimed at
     bringing together researchers interested in such a task.

     Topics of interest include, but are not limited to:

     * algorithmic approaches for distance metric(*) learning,
     * comparisons of distance metric learning(*) approaches,
     * effect of distance metric(*) learning on retrieval/categorization models,
     * learning a distance(*) robust to certain transformations,
     * links between distance(*) learning and the ranking/categorization problem,
     * criteria, loss bounds for distance(*) learning,
     * using unlabeled data for distance(*) learning,
     * applications of the above to IR/categorization problems for text, vision..

     (*) "distance metric" can, of course, be replaced by similarity measure,
         kernel or matching measure as mentioned in the abstract.

Submissions:

     The problem of "Learning to Compare Examples" (LCE) has
     arisen in several application contexts, ranging from
     information retrieval (e.g. [6]) to face verification
     (e.g. [1]) and it has been addressed relying on various
     machine learning approaches, which include, among others,
     Neural Networks (e.g. [1]), Kernel Methods (e.g. [6,8])
     and Boosting Approaches (e.g. [2]). Also, a large variety
     of learning criteria have been proposed for this task.
     This workshop aims at gathering the different communities
     that have been working on this problem, with the
     objective to foster collaboration and inspiration between
     them. In this context, contributions from researchers of
     these communities are solicited.

     Abstract Format

     We encourage the submissions of extended abstract. The
     suggested abstract length is ~3 pages (maximum 8 pages),
     formatted in the NIPS format. The authors of the accepted
     abstracts will be allocated between 20 and 40 minutes to
     present their work (to be determined according to
     submissions). In addition, the abstracts will be
     available to a broader audience on this web site.

     Submission Procedure

     The authors should submit their extended abstract to: lce [ @ ] idiap.ch
     in pdf before October 19, 2006, 23:59 Samoa Time. An email
     confirming the reception of the submission will be sent
     by the organizers.

     Planned Schedule

     Sept 28:         Workshop announcement / call for abstracts
     Oct 26:          Abstract submission deadline
     Nov 9:           Notification of acceptance
     Nov 23:          Final extended abstracts due
     Dec 8 or 9: Workshop

     Planned Program

     In addition to the peer-review presentations, two invited
     talks will also be given. Based on the dominant themes of
     the workshop submissions as well as the opinions of the
     PC members and the invited speakers, we plan to identify
     2/3 main themes related to the workshop topics in order
     to group the talks and organize thematic discussions
     after the presentations.

     Questions to the Organizers

     Requests, suggestions and comments should be sent to: lce [ @ ] idiap.ch

People

     Invited Speakers

        Yann LeCun, New York University
        Sam Roweis, University of Toronto

     Organizers

        Samy Bengio, IDIAP Research Institute
        David Grangier, IDIAP Research Institute

     Program Committee

        Samy Bengio, IDIAP Research Institute
        Gilles Blanchard, Fraunhofer FIRST
        Chris Burges, Microsoft Research
        David Grangier, IDIAP Research Institute
        Thomas Hofmann, Technical University of Darmstadt
        Guy Lebanon, Purdue University
        Thorsten Joachims, Cornell University
        Yoram Singer, The Hebrew University
        Alex Smola, National ICT Australia

References

   1. S. Chopra, R. Hadsell and Y. LeCun,
      Learning a Similarity Metric Discriminatively, with Application to Face
      Verification (CVPR, 2005).

   2. K. Crammer, J. Keshet and Y. Singer,
      Kernel Design using Boosting (NIPS, 2002).

   3. F. Fleuret and G. Blanchard,
      Pattern Recognition from One Example by Chopping (NIPS, 2005).

   4. A. Globerson and S. Roweis,
      Metric Learning by Collapsing Classes (NIPS, 2005).

   5. J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov,
      Neighbourhood Component Analysis (NIPS, 2004).

   6. D. R. Hardoon, S. Szedmak and J. Shawe-Taylor,
      Canonical Correlation Analysis: An Overview with Application to Learning
      Methods (Neural Comp., 2004).

   7. T. Hertz, A. Bar-Hillel and D. Weinshall,
      Boosting Margin-Based Distance Functions for Clustering (ICML, 2004).

   8. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui and M. I. Jordan,
      Learning the Kernel Matrix with Semidefinite Programming (JMLR, 2004).

   9. G. Lebanon,
      Metric Learning for Text Documents (TPAMI, 2006).

  10. S. Shalev-Shwartz, Y. Singer and A. Y. Ng,
      Online and Batch Learning of Pseudo-Metrics (ICML, 2004).

  11. M. Schutz and T. Joachims,
      Learning a Distance Metric from Relative Comparisons (NIPS, 2003).

  12. K. Q. Weinberger, J. Blitzer, and L. K. Saul,
      Distance Metric Learning for Large Margin Nearest Neighbor Classification
      (NIPS, 2005).

  13. E. Xing, A. Y. Ng, M. Jordan, and S. Russell,
      Distance Metric Learning, with Application to Clustering with
      Side-Information (NIPS, 2002).

Sponsors

    This workshop is partially sponsored by the PASCAL European Network of
    Excellence, in the context of the thematic on Intelligent Information Access.

----
Samy Bengio
Senior Researcher in Machine Learning.
IDIAP, CP 592, rue du Simplon 4, 1920 Martigny, Switzerland.
tel: +41 27 721 77 39, fax: +41 27 721 77 12.
mailto:bengio at idiap.ch, http://www.idiap.ch/~bengio