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 two 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 software.

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, Mikio Braun,Technische Universität Berlin
  • Cheng Soon Ong,ETH Zürich
  • Patrik Hoyer, Helsinki Institute for Information Technology