Advances on Neural Information Processing Systems

Learning to Compare Examples

NIPS'06 Workshop

December 8, 2006, Whistler, BC, Canada

Overview - Program - People - References


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.


Friday, December 8th 2006

Morning Session: 7:30am - 10:30am
Learning to Compare Examples
D. Grangier
8:00amInvited Talk
Learning Similarity Metrics with Invariance Properties
Y. LeCun
8:45amLearning Visual Distance Function for Object Identification from one Example
E. Nowak and F. Jurie
9:10amCoffee break
9:30amLearning to Compare using Operator-Valued Large-Margin Classifiers
A. Maurer
9:55amConformal Multi-Instance Kernels
M. B. Blaschko and T. Hofmann
Suggested Topic: Applications of Learning to Compare Examples

Afternoon Session: 3:30pm - 6:30pm
3:30pmInvited Talk
Neighbourhood Components Analysis and Metric Learning
S. Roweis
4:15pmFast Discriminative Component Analysis for Comparing Examples
J. Peltonen, J. Goldberger and S. Kaski
4:40pmInformation-Theoretic Metric Learning
J. Davis, B. Kulis, S. Sra and I. Dhillon
5:05pmCoffee break
5:25pmStatistical Translation, Heat Kernels, and Expected Distances
J. Dillon, Y. Mao, G. Lebanon and J. Zhang
5:50pmStructured Network Learning
S. Andrews and T. Jebara
Suggested Topic: Kernel and Distance Learning


Invited Speakers


Program Committee


  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).


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