NIPS Workshop on Machine Learning Open Source Software 2008

Workshop on Machine Learning Open Source Software 2008

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

Videos of talks are now available online

Important Dates

  • Submission Date: October 1st October 6th, 2008, Samoa time closed
  • Notification of Acceptance: October 21st, 2008
  • Workshop date: December 12th, 2008



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.

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.

Videos of talks are now available online

The workshop will be covered live at twitter to keep you up to date on the schedule.

For more information, have a look at page 67 of the official workshop brochure.

Morning session: 7:30am - ­10:30am

  • 07:30 Introduction and overview
  • 07:45 Octave (John W. Eaton)
  • Contributed Talks
  • 10:00 - 10:30 Discussion: What is a good mloss project?
    • Data exchange standards
    • Review criteria for JMLR mloss
    • Interoperable software
    • Test suites

Afternoon session: 3:30pm - ­6:30pm

Invited Speakers

pascal2 logo

  • John D. Hunter Main author of matplotlib. (confirmed)

    matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala matlab or mathematica), web application servers, and works with six graphical user interface toolkits. matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface or via a handle graphics interface familiar to matlab users.

  • John. W. Eaton Main author of Octave. (confirmed)

    GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with Matlab. It may also be used as a batch-oriented language.

Call for Contributionsclosed

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.

Program Committee

All confirmed


  • 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)