News
News 01/14/11: Video Lectures are Online
Click here to watch the video streams of the workshop presentations.
News 19/10/10: JMLR Special Topic on Kernel and Metric Learning
The Journal of Machine Learning Research invites submissions to the special topic on Kernel and Metric Learning. Please see the Call for Papers for further information and submission instructions.
News 01/09/10: Call for Submissions
Submissions are solicited for a Multiple Kernel Learning workshop to be held on December 11th, 2010 at this year's NIPS workshop session in Whistler, Canada.
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 (JMLR). See the Call for Papers for the JMLR Special Topic on Kernel and Metric Learning.
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. NIPS 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
committee.
Please send your submission by email to ml-newtrendsinmkl@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.
Workshop Description
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:
- 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.
- 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.
Schedule
Morning Session
| 7:30am | Introduction, overview, and open problems |
|---|---|
| 7:40am | Various Formulations for Learning the Kernel and Structured Sparsity Massimiliano Pontil (Invited Speaker) |
| 8:10am | A Gaussian Process View on MKL Raquel Urtasun (Invited Speaker) |
| 8:40am | Regularization Strategies and Empirical Bayesian Learning for MKL Ryota Tomioka, Taiji Suzuki |
| 9:00am | Coffee break and poster session |
| 9:30am | Online MKL for Structured Prediction Andre F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mario A.T. Figueiredo |
| 9:50am | Multiple Gaussian Process Models Cedric Archambeau, Francis Bach |
| 10:10am | Multitask Multiple Kernel Learning (MT-MKL) Chris Widmer, Nora C. Toussaint, Yasemin Altun, Gunnar Raetsch |
| 10:30am | Break until afternoon session |
Afternoon Session
| 3:30pm | Structured Regularization for MKL Guillaume Obozinski (Invited Speaker) |
|---|---|
| 4:00pm | Distance Metric Learning for Kernel Machines Kilian Q. Weinberger (Invited Speaker) |
| 4:30pm | Poster Spotlights Multiple Kernel Testing for SVM-based System Identification Matthew Higgs, John Shawe-Taylor Supervised and Localized Dimensionality Reduction from Multiple Feature Representations or Kernels Mehmet Goenen, Ethem Alpaydin Multiple Kernel Learning for Efficient Conformal Predictions Vineeth N Balasubramanian, Shayok Chakraborty, Sethuraman Panchanathan Operator Induced Multi-Task Gaussian Processes for Solving Differential Equations Arman Melkumyan Learning Kernels via Margin-and-Radius Ratios Kun Gai, Guangyun Chen, and Changshui Zhang Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features Tristan Fletcher, Zakria Hussain, John Shawe-Taylor |
| 4:50pm | Coffee break and poster session continued |
| 5:30pm | Co-regularized Spectral Clustering with Multiple Kernels Abhishek Kumar, Piyush Rai, Hal Daume III |
| 5:50pm | Panel discussion |
| 6:30pm | Closing Remarks |
Contact:
Organizers:
- Marius Kloft
- UC Berkeley
- Ulrich Rückert
- UC Berkeley
- Cheng Soon Ong
- ETH Zürich
- Alain Rakotomamonjy
- University of Rouen
- Sören Sonnenburg
- FML of the Max Planck Society / TU Berlin
- Francis Bach
- INRIA / ENS
PASCAL2 Invited Speakers:
Important Dates:
- Submission Deadline: Oct 18
- Notification Sent: Nov 2
- Workshop Date: Dec. 11
