Extended Deadline: ICML Workshop on Machine Learning Open Source Software 2010

To accomodate researchers waiting for decisions on their ICML papers (due
April 16) before committing to travel to Haifa, the submission deadline for the
Machine Learning Open Source Software (MLOSS) 2010 workshop has been
extended to April 23. As a result of this, we have also pushed back the
acceptance notification to May 8.


Call for Contributions

Workshop on Machine Learning Open Source Software 2010

at ICML 2010, Haifa, Israel,
25th of June, 2010


The ICML workshop on Workshop on Machine Learning Open Source Software
(MLOSS) will held in Haifa, Israel on the 25th of June 2010.

Important Dates

* Submission Date: April 23rd, 2010
* Notification of Acceptance: May 8th, 2010
* Workshop date: June 25th, 2010

Call for Contributions

The organizing committee is currently seeking abstracts for talks
at MLOSS 2010. 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 icml2010

* 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 April 23rd 2010, we will collect all projects tagged with icml2010
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. To date 11 machine learning open source
software projects have been published in JMLR. 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 221 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: Gary Bradski and Victoria Stodden.

* 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 ICML, decisions and agreements taken at the workshop will have
the potential to significantly impact the future of machine learning

Invited Speakers

* Gary Bradski One of the main authors of OpenCV. (tentatively

Gary Bradski was previously responsible for the Open Source
Computer Vision Library (OpenCV) that is used globally in
research, government and commercial applications. He has also
been responsible for the open source statistical Machine
Learning Library and the Probabilistic Network Library. More
recently Dr. Bradski led the vision team for Stanley, the
Stanford robot that won the DARPA Grand Challenge autonomous
race in 2005 and most recently helped found the Stanford
Artificial Intelligence Robot (STAIR) project under the
leadership of Professor Andrew Ng. Dr. Bradski recently published
a new book for O’Reilly Press: Learning OpenCV: Computer Vision
with the OpenCV Library.

* Victoria Stodden

Victoria Stodden is a Postdoctoral Associate in Law and a Kauffman
Fellow in Law at the Information Society Project at Yale Law
School. After completing her PhD in statistics at Stanford
University in 2006 with advisor David Donoho, she obtained a
Master in Legal Studies in 2007 from Stanford Law School. She is
developing a new licensing structure for computational research
and author of the award winning paper “Reproducible Research
Standard” that describes her ideas.

Workshop 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: 09:00 – 12:00

* Introduction and overview
* Contributed Talks
* Invited Talk: OpenCV (Gary Bradski)
* Contributed Talks
* Discussion: Exchanging Software and Data

Afternoon session: 14:00 – 17:00

* Contributed Talks
* Invited Talk: The Reproducible Research Standard
(Victoria Stodden)
* Discussion: Reproducible research

Program Committee

* Jason Weston (Google Research, NY, USA)
* Leon Bottou (NEC Princeton, USA)
* Tom Fawcett (Stanford Computational Learning Laboratory, USA)
* Sebastian Nowozin (Microsoft Research, UK)
* Konrad Rieck (Technische Universität Berlin, 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)
* Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
* Geoff Holmes (University of Waikato, New Zealand)
* Peter Reutemann (University of Waikato, New Zealand)
* Markus Weimer (Yahoo Research, California, USA)
* Alain Rakotomamonjy (University of Rouen, France)


* Soeren Sonnenburg,
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9,
10587 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

* Patrik Hoyer
Helsinki Institute for Information Technology,
Gustaf Hällströmin katu 2b, 00560 Helsinki, Finland


The workshop is supported by PASCAL (Pattern Analysis, Statistical
Modelling and Computational Learning)