Tuesday, April 28, 2015

ICML Workshop on Machine Learning Open Source Software 2015: Open Ecosystems

This is just a short notice but since Open Source Machine Learning software development is so important, I have decided to run this here today (please note the deadline). 
The ICML Workshop on Machine Learning Open Source Software (MLOSS) will held in Lille, France on the 10th of July, 2015.

Important Dates

  • Submission Date: 28 April 2015, 23:59 UTC
  • Notification of Acceptance: 11 May 2015
  • Workshop date: 10 July 2015
Note that the submission deadline is a few days earlier than the ICML recommended deadline. This is to give our program committee a reasonable amount of time to review your submission.


Machine learning open source software (MLOSS) is one of the cornerstones of open science and reproducible research. Along with open access and open data, it enables free reuse and extension of current developments in machine learning. The mloss.org site exists to support a community creating a comprehensive open source machine learning environment, mainly by promoting new software implementations. This workshop aims to enhance the environment by fostering collaboration with the goal of creating tools that work with one another. Far from requiring integration into a single package, we believe that this kind of interoperability can also be achieved in a collaborative manner, which is especially suited to open source software development practices.
The workshop is aimed at all machine learning researchers who wish to have their algorithms and implementations included as a part of the greater open source machine learning environment. Continuing the tradition of well received workshops on MLOSS at NIPS 2006, NIPS 2008, ICML 2010 and NIPS 2013, we plan to have a workshop that is a mix of invited speakers, contributed talks and discussion/activity sessions. For 2015, we focus on building open ecosystems. Our invited speakers will illustrate the process for Python and Julia through presenting modern high-level high-performance computation engines, and we encourage submissions that showcase the benefits of multiple tools in the same ecosystem. All software presentations are required to include a live demonstration. The workshop will also include an active session (“hackathon”) for planning and starting to develop infrastructure for measuring software impact.
We have two confirmed invited speakers
  • John Myles White (Facebook), lead developer of Julia statistics and machine learning (confirmed): “Julia for machine learning: high-level syntax with compiled-code speed”.
  • Matthew Rocklin (Continuum Analytics), developer of Python computational tools, in particular Blaze (confirmed): “Blaze, a modern numerical engine with out-of-core and out-of-order computations”.

Tentative Programme

  • 2 hours of invited talks consisting of 30 minute tutorial (Gaël Varoquaux) and 2*45 min invited talks (John Myles White and Matthew Rocklin). Both invited speakers have confirmed that they will attend.
  • 2 hours of submitted projects (contributed talks including a demo or spotlights + parallel demo session, depending on the number of high quality submissions)
  • 1 hour unconference-style open discussion (topics voted by workshop participants)
  • 1 hour hackathon/activity session on developing measurement of software impact
  • 2*30 minute coffee breaks

Call for Contributions

The organizing committee is currently seeking abstracts for talks at MLOSS 2015. MLOSS is a great opportunity for you to tell the community about your use, development, philosophy, or other activities related to open source software in machine learning. The committee will select several submitted abstracts for 20-minute talks.
All submissions must be made to https://www.easychair.org/conferences/?conf=mloss2015

Submission types

1. Software packages
Similar to the MLOSS track at JMLR, this includes (but is not limited to) numeric packages (as e.g. R, Octave, Python), machine learning toolboxes and implementations of ML-algorithms.
Submission format: 1 page abstract which must contain a link to the project description on mloss.org. Any bells and whistles can be put on your own project page, and of course provide this link on mloss.org.
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. If accepted, the presentation must include a software demo.
2. ML related projects
As the theme for this year is open ecosystems, projects of a more general nature such as software infrastructure or tools for general data analysis are encouraged. This category is open for position papers, interesting projects and ideas that may not be new software themselves, but link to machine learning and open source software.
Submission format: abstract with no page limit. Please note that there will be no proceedings, i.e. the abstracts will not be published.
We look forward for submissions that are novel, exciting and that appeal to the wider community.

Program Committee

  • Asa Ben-Hur (Colorado State University)
  • Mathieu Blondel (NTT Communication Science Laboratories)
  • Mikio Braun (Technical University of Berlin)
  • Ryan Curtin (Georgia Tech)
  • Alexandre Gramfort (Telecom ParisTech)
  • Ian Goodfellow (Google)
  • James Hensman (University of Sheffield)
  • Laurens van der Maaten (Facebook AI Research)
  • Andreas Müller (New York University)
  • Mark Reid (Australian National University)
  • Peter Reutemann (University of Waikato)
  • Konrad Rieck (University of Göttingen)
  • Conrad Sanderson (NICTA)
  • Heiko Strathmann (University College London)
  • Ameet Talwalkar (University of California LA)
  • Lieven Vandenberghe (University of California LA)
  • Aki Vehtari (Aalto University)
  • Markus Weimer (Microsoft Research)


  • Gaël Varoquaux
    INRIA, France
  • Antti Honkela
    University of Helsinki, Helsinki Institute for Information Technology HIIT, Helsinki, Finland
  • Cheng Soon Ong
    Machine Learning Research Group, NICTA, Canberra, Australia
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