Workshop on Machine Learning Open Source 2008

. Wednesday, September 10, 2008
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I like Open Access and Open Software, in fact, I'm meber of the local LUG of my University (GLUEM) and some of my posts are refered to these topics. In ML-News list, I've seen a call for submissiones for the a Workshop on Machine Learning Open Source (MLOSS), that will be held at NIPS, December 12th. I this this kind of workshops are a very good idea to promote the use of Open Software in ML, and give extra benefits to those developers that let the community use their software, allowing other researchers a faster development of their experiments.

The NIPS workshop on Workshop on Machine Learning Open Source Software (MLOSS) will held in Whistler (B.C.) on the 12th of December, 2008.

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 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

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. ) 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 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 software.

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 datasets.

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, USA)
* 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)