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:
(*) "distance metric" can, of course, be replaced by similarity measure,
kernel or matching measure as mentioned in the abstract.
- 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...
Friday, December 8th 2006
Morning Session: 7:30am - 10:30am
Learning to Compare Examples
Learning Similarity Metrics with Invariance Properties
|8:45am||Learning Visual Distance Function for Object Identification from one Example|
E. Nowak and F. Jurie
|9:30am||Learning to Compare using Operator-Valued Large-Margin Classifiers|
|9:55am||Conformal Multi-Instance Kernels|
M. B. Blaschko and T. Hofmann
Suggested Topic: Applications of Learning to Compare Examples
Afternoon Session: 3:30pm - 6:30pm
Neighbourhood Components Analysis and Metric Learning
|4:15pm||Fast Discriminative Component Analysis for Comparing Examples|
J. Peltonen, J. Goldberger and S. Kaski
|4:40pm||Information-Theoretic Metric Learning|
J. Davis, B. Kulis, S. Sra and I. Dhillon
|5:25pm||Statistical Translation, Heat Kernels, and Expected Distances|
J. Dillon, Y. Mao, G. Lebanon and J. Zhang
|5:50pm||Structured Network Learning|
S. Andrews and T. Jebara
Suggested Topic: Kernel and Distance Learning
- 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. Shawe-Taylor,
Canonical Correlation Analysis: An Overview with Application to Learning Methods (Neural Comp., 2004).
- T. Hertz, A. Bar-Hillel and D. Weinshall,
Boosting Margin-Based 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. Shalev-Shwartz, Y. Singer and A. Y. Ng,
Online and Batch Learning of Pseudo-Metrics (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 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.