[pymvpa] [Fwd: Fwd: [ML-news] Call for Submissions: Workshop on Machine Learning Open Source Software (MLOSS), NIPS*08]

Emanuele Olivetti emanuele at relativita.com
Tue Sep 9 12:43:16 UTC 2008

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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