NIPS 2010 Workshop: New Directions in Multiple Kernel Learning – Call for Contributions

New Directions in Multiple Kernel Learning
NIPS 2010 Workshop, Whistler, British Columbia, Canada
— Submission Deadline: October 18, 2010 —

Research on Multiple Kernel Learning (MKL) has matured to the point
where efficient systems can be applied out of the box to various
application domains. In contrast to last year’s workshop, which
evaluated the achievements of MKL in the past decade, this workshop
looks beyond the standard setting and investigates new directions for

In particular, we focus on two topics:
1. There are three research areas, which are closely related, but have
traditionally been treated separately: learning the kernel, learning
distance metrics, and learning the covariance function of a Gaussian
process. We therefore would like to bring together researchers from
these areas to find a unifying view, explore connections, and
exchange ideas.
2. We ask for novel contributions that take new directions, propose
innovative approaches, and take unconventional views. This includes
research, which goes beyond the limited classical sum-of-kernels
setup, finds new ways of combining kernels, or applies MKL in more
complex settings.

The workshop will include:
* A brief introduction talk
* 4 invited keynote talks on new views and directions in MKL
* 4 talks by authors of contributed papers
* A poster session of contributed papers, and a poster-spotlight
* A discussion panel

The organizing committee is seeking short research papers for
presentation at the workshop. The committee will select several
submitted papers for 15-minute talks and poster presentations. The
accepted papers will be published on the workshop web site.

We plan to publish proceedings of this workshop in a special issue of an
appropriate journal. We will submit a proposal for such an issue to the
Journal of Machine Learning Research.

Amongst others, we encourage submissions in the following areas:
* New views on MKL, e.g., from the perspectives of metric learning,
Gaussian processes, learning with similarity functions, etc.
* New approaches to MKL, in particular, kernel parameterizations
different than convex combinations and new objective functions
* Sparse vs. non-sparse regularization in similarity learning
* Use of MKL in unsupervised, semi-supervised, multi-task, and
transfer learning
* MKL with structured input/output
* Innovative applications

Submissions should be written as extended abstracts, no longer than 4
pages in the NIPS latex style. Style files and formatting instructions
can be found at The
extended abstract may be accompanied by an unlimited appendix and
other supplementary material, with the understanding that anything
beyond 4 pages may be ignored by the program committee.

Please send your submission by email to
before October 18. Notifications will be given on Nov 2. Topics that
were recently published or presented elsewhere are allowed, provided
that the extended abstract mentions this explicitly.

Marius Kloft (UC Berkeley), Ulrich Rueckert (UC Berkeley),
Cheng Soon Ong (ETH Zuerich), Alain Rakotomamonjy (University of
Rouen), Soeren Sonnenburg (TU Berlin/Max Planck FML), Francis Bach