========================================================================= CALL FOR PAPERS New Directions in Multiple Kernel Learning NIPS 2010 Workshop, Whistler, British Columbia, Canada http://doc.ml.tu-berlin.de/mkl_workshop -- 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 MKL. 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 session * 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 SUBMISSION GUIDELINES 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 http://nips.cc/PaperInformation/StyleFiles. 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 ml-newtrendsin...@lists.tu-berlin.de 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. ORGANIZERS: 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 (ENS/INRIA) WORKSHOP HOMEPAGE: http://doc.ml.tu-berlin.de/mkl_workshop _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai