[pymvpa] [Fwd: Fwd: [ML-news] Call for Submissions: Workshop on Machine Learning Open Source Software (MLOSS), NIPS*08]
debian at onerussian.com
Tue Sep 9 14:27:04 UTC 2008
sounds like an interesting workshop might it be... needless to say that
Whistler is a really neat ski resort...
anyone interested for a far trip?
On Tue, 09 Sep 2008, Emanuele Olivetti wrote:
> Maybe of interest.
> -------- Original Message --------
> ---------- Forwarded message ----------
> From: mikiobraun <mikiobraun at goo***.com>
> Date: 2008/9/8
> Subject: [ML-news] Call for Submissions: Workshop on Machine Learning
> Open Source Software (MLOSS), NIPS*08
> To: Machine Learning News <ML-news at googlegroups.com>
> Call for Submissions
> Workshop on Machine Learning Open Source Software 2008
> held at NIPS*08, Whistler, Canada,
> December 12th, 2008
> The NIPS workshop on Workshop on Machine Learning Open Source Software
> (MLOSS) will held in Whistler (B.C.) on the 12th of December,
> Important Dates
> * Submission Date: October 1st, 2008
> * Notification of Acceptance: October 14th, 2008
> * Workshop date: December 12 or 13th, 2008
> Call for Contributions
> The organizing committee is currently seeking abstracts for talks at
> MLOSS 2008. MLOSS is a great opportunity for you to tell the community
> about your use, development, or philosophy of open source software in
> machine learning. This includes (but is not limited to) numeric
> packages (as e.g. R,octave,numpy), machine learning toolboxes and
> implementations of ML-algorithms. The committee will select several
> submitted abstracts for 20-minute talks. The submission process is
> very simple:
> * Tag your mloss.org project with the tag nips2008
> * Ensure that you have a good description (limited to 500 words)
> * Any bells and whistles can be put on your own project page, and
> of course provide this link on mloss.org
> On 1 October 2008, we will collect all projects tagged with nips2008
> for review.
> Note: Projects must adhere to a recognized Open Source License
> (cf. http://www.opensource.org/licenses/ ) and the source code must
> have been released at the time of submission. Submissions will be
> reviewed based on the status of the project at the time of the
> submission deadline.
> We believe that the wide-spread adoption of open source software
> policies will have a tremendous impact on the field of machine
> learning. The goal of this workshop is to further support the current
> developments in this area and give new impulses to it. Following the
> success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the
> Journal of Machine Learning Research (JMLR) has started a new track
> for machine learning open source software initiated by the workshop's
> organizers. Many prominent machine learning researchers have
> co-authored a position paper advocating the need for open source
> software in machine learning. Furthermore, the workshop's organizers
> have set up a community website mloss.org where people can register
> their software projects, rate existing projects and initiate
> discussions about projects and related topics. This website currently
> lists 123 such projects including many prominent projects in the area
> of machine learning.
> The main goal of this workshop is to bring the main practitioners in
> the area of machine learning open source software together in order to
> initiate processes which will help to further improve the development
> of this area. In particular, we have to move beyond a mere collection
> of more or less unrelated software projects and provide a common
> foundation to stimulate cooperation and interoperability between
> different projects. An important step in this direction will be a
> common data exchange format such that different methods can exchange
> their results more easily.
> This year's workshop sessions will consist of three parts.
> * We have two invited speakers: John Eaton, the lead developer of
> Octave and John Hunter, the lead developer of matplotlib.
> * Researchers are invited to submit their open source project to
> present it at the workshop.
> * In discussion sessions, important questions regarding the future
> development of this area will be discussed. In particular, we
> will discuss what makes a good machine learning software project
> and how to improve interoperability between programs. In
> addition, the question of how to deal with data sets and
> reproducibility will also be addressed.
> Taking advantage of the large number of key research groups which
> attend NIPS, decisions and agreements taken at the workshop will have
> the potential to significantly impact the future of machine learning
> Invited Speakers
> * John D. Hunter - Main author of matplotlib.
> * John W. Eaton - Main author of Octave.
> Tentative Program
> The 1 day workshop will be a mixture of talks (including a mandatory
> demo of the software) and panel/open/hands-on discussions.
> Morning session: 7:30am - 10:30am
> * Introduction and overview
> * Octave (John W. Eaton)
> * Contributed Talks
> * Discussion: What is a good mloss project?
> o Review criteria for JMLR mloss
> o Interoperable software
> o Test suites
> Afternoon session: 3:30pm - 6:30pm
> * Matplotlib (John D. Hunter)
> * Contributed Talks
> * Discussion: Reproducible research
> o Data exchange standards
> o Shall datasets be open too? How to provide access to
> data sets.
> o Reproducible research, the next level after UCI
> Program Committee
> * Jason Weston (NEC Princeton, USA)
> * Gunnar Rätsch (FML Tuebingen, Germany)
> * Lieven Vandenberghe (University of California LA, USA)
> * Joachim Dahl (Aalborg University, Denmark)
> * Torsten Hothorn (Ludwig Maximilians University, Munich, Germany)
> * Asa Ben-Hur (Colorado State University, USA)
> * William Stafford Noble (Department of Genome Sciences Seattle,
> * Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
> * Geoff Holmes (University of Waikato, New Zealand)
> * Alain Rakotomamonjy (University of Rouen, France)
> * Soeren Sonnenburg
> Fraunhofer FIRST Kekuléstr. 7, 12489 Berlin, Germany
> * Mikio Braun
> Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587
> Berlin, Germany
> * Cheng Soon Ong
> ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland
> The workshop is supported by PASCAL (Pattern Analysis, Statistical
> Modelling and Computational Learning)
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