Workshop on Machine Learning Open Source Software 2013: Towards Open Workflows
Submission Date: October 9th, 2013, Samoa time(past) Notification of Acceptance: October 23rd, 2013(past)
- Workshop date: Tuesday, December 10th, 2013
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 and ICML 2010, we plan to have a workshop that is a mix of invited speakers, contributed talks and discussion sessions. For 2013, we focus on workflows and pipelines. Many algorithms and tools have reached a level of maturity which allows them to be reused and integrated into larger systems.
We have two confirmed invited speakers
- Gary Bradski is the Vice President of Computer Vision and Machine Learning, Magic Leap Inc. and President and CEO of OpenCV Foundation, which manages the development of the OpenCV machine learning and computer vision library.
- Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. He is the creator of the interactive computing system IPython and continues to lead its development.
Morning Session (7:30-10:30)
- Opening remarks
- 7:35-8:20 Invited speaker: Gary Bradski, openCV
- 8:20-8:40 Factorie
- 8:40-9:00 pySPACE
- 9:00 - 9:30 Coffee break
- 9:30 - 10:30 Demos (15 min for highlights)
Afternoon Session (3:30-6:30)
- 3:30-4:15 Invited speaker: Fernando Perez, ipython
- 4:15-4:35 scikit-learn
- 4:35-4:55 rOpenGov
- 4:55-5:25 Coffee break
- 5:25-6:25 Discussion
- collaboration and interaction between projects
- building pipelines and workflows
- managing components
- libraries and data formats
- hosting projects and collaborative development
- user/developer interfaces for machine learning
- command line / gui, scripting
- future of MLOSS workshops and MLOSS community
- GSoC 2014
- next MLOSS workshop theme
- improvements to mloss.org and JMLR mloss
- 6:25-6:30 Closing remarks
The organizing committee is currently seeking abstracts for talks at MLOSS 2013. 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=mloss2013
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. Other submissions
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.
- Asa Ben-Hur (Colorado State University)
- Mikio Braun (Technical University of Berlin)
- James Hensman (University of Sheffield)
- Geoffrey Holmes (University of Waikato)
- Torsten Hothorn (University of Zurich)
- Balász Kegl (University of Paris-Sud)
- Joris M. Mooij (University of Amsterdam)
- Mark Reid (Australian National University)
- Peter Reutemann (University of Waikato)
- Konrad Rieck (University of Göttingen)
- Lieven Vandenberghe (University of California LA)
- Markus Weimer (Microsoft Research)
- Jason Weston (Google Research)