REMINDER – CfP: JMLR Special Topic on Kernel and Metric Learning

Multiple Kernel Learning (MKL) has received significant interest in
the machine learning community. It is reaching a point where efficient
systems can be applied out of the box to various application domains,
and several methods have been proposed to go beyond canonical convex
combinations. Concurrently, research in the area of metric learning
has also progressed significantly, and researchers are applying them
to various problems in supervised and unsupervised learning. A common
theme is that one can use data to infer similarities between objects
while simultaneously solving the machine learning task.

A special topic of the Journal of Machine Learning Research will be
devoted to kernel and metric learning with a special emphasis on new
directions and connections between the various related areas; like
learning the kernel, learning metrics, and learning the covariance
function of a Gaussian process. We invite researchers to submit novel
and interesting contributions to this special issue. Further
information can be found at .

Important dates

Submission: 1 March 2011
Decision: 1 May 2011
Final versions: 1 July 2011

Topics of Interest
Topics of interest include:

* New approaches to MKL, in particular, kernel parameterizations
different than convex combinations and new objective functions
* New connections between kernel, metric and covariance learning,
e.g., from the perspectives of Gaussian processes, learning with
similarity functions, etc.
* Sparse vs. non-sparse regularization in similarity learning
* Efficient algorithms for metric learning
* Use of MKL in unsupervised, semi-supervised, multi-task, and
transfer learning
* MKL with structured input/output
* Innovative applications

Submission procedure
Authors are kindly invited to follow the standard JMLR format and
submission procedure JMLR submission format, the number of pages is
limited to 30. Please include a note stating that your submission is for
the special topic on Multiple Kernel Learning.

Soeren Sonnenburg, Berlin Institute of Technology, Berlin, Germany
Francis Bach, INRIA and Ecole Normale Superieure, Paris, France
Cheng Soon Ong, ETH, Zurich, Switzerland