CfP: NIPS 2013 Workshop on Machine Learning Open Source Software: Towards Open Workflows

CfP: NIPS 2013 Workshop on Machine Learning Open Source Software: Towards Open Workflows

Posted by Rebecca Martin on Wed, 28/08/2013 - 10:18

Call for Contributions

Workshop on Machine Learning Open Source Software 2013:
Towards Open Workflows

at NIPS 2013, Lake Tahoe, Nevada, United States,
9th or 10th December, 2013

The NIPS Workshop on Machine Learning Open Source Software (MLOSS) will held in Lake Tahoe (NV) on the 9th or 10th of December, 2013. 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.

Important Dates

* Submission Date: October 9th, 2013
* Notification of Acceptance: October 23rd, 2013
* Workshop date: December 9th or 10th, 2013

Call for Contributions

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.

Submission Types

1. Software packages

This includes (but is not limited to) numeric packages (as e.g. R, Octave, Python), machine learning toolboxes and implementations of ML-algorithms, similar to the MLOSS track at JMLR ( ).

Submission format: 1 page abstract which must contain a link to the project description on Any bells and whistles can be put on your own project page, and of course provide this link on

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. 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. For more details see:

Please submit your contributions at


* Antti Honkela
University of Helsinki, Helsinki Institute for Information
Technology HIIT, Helsinki, Finland

* Cheng Soon Ong
NICTA, Victoria Research Laboratory, Melbourne, Australia