Call for contributions: NIPS 2009 Workshop on Understanding Multiple Kernel Learning Methods
Gert Lanckriet
Call for contributions - Understanding Multiple Kernel Learning Methods
Workshop at the Twenty-Third Annual Conference on Neural Information
Processing Systems (NIPS 2009), Whistler, BC, Canada, December 11 or
12, 2009.
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DESCRIPTION
Multiple kernel learning has been the subject of nearly a decade of
research. Designing and integrating kernels has proven to be an
appealing approach to address several, challenging real world
applications, involving multiple, heterogeneous data sources in
computer vision, bioinformatics, audio processing problems, etc. The
goal of this workshop is to step back and evaluate the achievements
of multiple kernel learning in the past decade, covering a variety of
applications.
In short, this workshop seeks to understand where and how kernel
learning is relevant (with respect to accuracy, interpretability,
feature selection, etc.), rather than exploring the latest
optimization techniques and extension formulations. More
specifically, the workshop envisions to discuss the following two
questions:
-- 1 -- Kernel learning vs. kernel design: Does kernel learning offer
a practical advantage over the manual design of kernels?
-- 2 -- Given a set of kernels, what is the optimal way, if any, to
combine them (sums, products, learned or non learned, with or without
cross-validation)?
We are seeking participants interested in presenting their work and
relating their experience in the workshop, providing insight on the
above two questions. This includes evidence of MKL improving accuracy
beyond any existing method based on single kernels (provided with
insights as to why there is such improvement), as well as evidence of
the opposite (with insights as to why). We welcome presentation of
recent results, as well as presentations based on previously
published work that shed light on the above questions.
If you are interested in participating and contributing a
presentation, please send an email to bmc...@cs.ucsd.edu with an
abstract or a summary. If the presentation is based on previously
published work, please include details of such publications.
REPOSITORY
In conjunction with the workshop, we are establishing an open
repository of data sets for use with MKL algorithms. Authors are
encouraged to contribute data to the MKL Repository (mkl.ucsd.edu),
and use the repository to benchmark new algorithms.
ORGANIZERS
* Gert Lanckriet (University of California, San Diego),
ge...@ece.ucsd.edu
* Francis Bach (Ecole Normale Superieure/INRIA),
franci...@ens.fr
* Nathan Srebro (Toyota Technological Institute, Chicago),
na...@uchicago.edu
* Brian McFee (University of California, San Diego),
bmc...@cs.ucsd.edu
Gert Lanckriet
Call for contributions - Understanding Multiple Kernel Learning Methods
http://mkl.ucsd.edu/workshop
Workshop at the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS 2009), Whistler, BC, Canada, December 11 or 12, 2009.
Multiple kernel learning has been the subject of nearly a decade of research. Designing and integrating kernels has proven to be an appealing approach to address several, challenging real world applications, involving multiple, heterogeneous data sources in computer vision, bioinformatics, audio processing problems, etc. The goal of this workshop is to step back and evaluate the achievements of multiple kernel learning in the past decade, covering a variety of applications.
In short, this workshop seeks to understand where and how kernel learning is relevant (with respect to accuracy, interpretability, feature selection, etc.), rather than exploring the latest optimization techniques and extension formulations. More specifically, the workshop envisions to discuss the following two questions:
-- 1 -- Kernel learning vs. kernel design: Does kernel learning offer a practical advantage over the manual design of kernels?
-- 2 -- Given a set of kernels, what is the optimal way, if any, to combine them (sums, products, learned or non learned, with or without cross-validation)?
We are seeking participants interested in presenting their work and relating their experience in the workshop, providing insight on the above two questions. This includes evidence of MKL improving accuracy beyond any existing method based on single kernels (provided with insights as to why there is such improvement), as well as evidence of the opposite (with insights as to why). We welcome presentation of recent results, as well as presentations based on previously published work that shed light on the above questions.