
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 kernelbased 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 apriori, relying on some knowledge/assumptions specific to
the task. An alternative to this apriori 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.


Friday, December 8th 2006
Morning Session: 7:30am  10:30am
7:30am  Introduction Learning to Compare Examples D. Grangier  [slides] 
8:00am  Invited Talk Learning Similarity Metrics with Invariance Properties Y. LeCun  [slides] 
8:45am  Learning Visual Distance Function for Object Identification from one Example E. Nowak and F. Jurie  [paper] [slides] 
9:10am  Coffee break  
9:30am  Learning to Compare using OperatorValued LargeMargin Classifiers A. Maurer  [paper] [slides] 
9:55am  Conformal MultiInstance Kernels M. B. Blaschko and T. Hofmann  [paper] [slides] 
10:20am  Discussion Suggested Topic: Applications of Learning to Compare Examples  
Afternoon Session: 3:30pm  6:30pm
3:30pm  Invited Talk Neighbourhood Components Analysis and Metric Learning S. Roweis  [paper] [slides] 
4:15pm  Fast Discriminative Component Analysis for Comparing Examples J. Peltonen, J. Goldberger and S. Kaski  [paper] [slides] 
4:40pm  InformationTheoretic Metric Learning J. Davis, B. Kulis, S. Sra and I. Dhillon  [paper] [slides] 
5:05pm  Coffee break  
5:25pm  Statistical Translation, Heat Kernels, and Expected Distances J. Dillon, Y. Mao, G. Lebanon and J. Zhang  [paper] [slides] 
5:50pm  Structured Network Learning S. Andrews and T. Jebara  [paper] [slides] 
6:15pm  Discussion Suggested Topic: Kernel and Distance Learning  


Invited Speakers
Organizers
Program Committee
 Samy Bengio, IDIAP Research Institute

 Gilles Blanchard, Fraunhofer FIRST
 Chris Burges, Microsoft Research
 François Fleuret, EPFL
 David Grangier, IDIAP Research Institute
 Thomas Hofmann, Google
 Guy Lebanon, Purdue University
 Thorsten Joachims, Cornell University
 Yoram Singer, The Hebrew University
 Alex Smola, National ICT Australia


 S. Chopra, R. Hadsell and Y. LeCun,
Learning a Similarity Metric Discriminatively, with Application to Face Verification (CVPR, 2005).
 K. Crammer, J. Keshet and Y. Singer,
Kernel Design using Boosting (NIPS, 2002).
 F. Fleuret and G. Blanchard,
Pattern Recognition from One Example by Chopping (NIPS, 2005).
 A. Globerson and S. Roweis,
Metric Learning by Collapsing Classes (NIPS, 2005).
 J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov,
Neighbourhood Component Analysis (NIPS, 2004).
 D. R. Hardoon, S. Szedmak and J. ShaweTaylor,
Canonical Correlation Analysis: An Overview with Application to Learning Methods (Neural Comp., 2004).
 T. Hertz, A. BarHillel and D. Weinshall,
Boosting MarginBased Distance Functions for Clustering (ICML, 2004).
 G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui and M. I. Jordan,
Learning the Kernel Matrix with Semidefinite Programming (JMLR, 2004).
 G. Lebanon,
Metric Learning for Text Documents (TPAMI, 2006).
 S. ShalevShwartz, Y. Singer and A. Y. Ng,
Online and Batch Learning of PseudoMetrics (ICML, 2004).
 M. Schutz and T. Joachims,
Learning a Distance Metric from Relative Comparisons (NIPS, 2003).
 K. Q. Weinberger, J. Blitzer, and L. K. Saul,
Distance Metric Learning for Large Margin Nearest Neighbor Classification (NIPS, 2005).
 E. Xing, A. Y. Ng, M. Jordan, and S. Russell,
Distance Metric Learning, with Application to Clustering with SideInformation (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.
