Official Website

http://mloss.org/

Background

Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for a wide range of applications. Inspired by similar efforts in bioinformatics (BOSC) or statistics (useR), our aim is to build a forum for open source software in machine learning.

  • If you want more background about why open source software is important for machine learning, read our position paper about the need for open source software in machine learning.
  • If you have written machine learning software, consider adding it to the projects at mloss.org.
  • In case your machine learning software can be considered a useful, mature piece of work consider a submission to the JMLR track for machine learning open source software.

Goals

Our goal is to support a community creating a comprehensive open source machine learning environment. Ultimately, open source machine learning software should be able to compete with existing commercial closed source solutions. To this end, it is not enough to bring existing and freshly developed toolboxes and algorithmic implementations to people’s attention. More importantly the MLOSS platform will facilitate collaborations with the goal of creating a set of 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.

Machine Learning Open Source Software 2015: Open Ecosystems in ICML 2015

Workshop on Machine Learning Open Source Software 2015: Open Ecosystems

The ICML Workshop on Machine Learning Open Source Software (MLOSS) will held in Lille, France on the 10th of July, 2015.

Description

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.