Call for papers: Journal of Machine Learning Research , Special Topic on Large Scale Learning – Dedline Extension

With the exceptional increase in computing power, storage capacity and network bandwidth of the past decades, ever growing datasets are collected in fields such as bioinformatics (Splice Sites, Gene Boundaries, etc), IT-security (Network traffic) or Text-Classification (Spam vs. Non-Spam), to name but a few. While the data size growth leaves computational methods as the only viable way of dealing with data, it poses new challenges; specifically, most machine learning algorithms hardly scale up beyond 1,000,000 examples or dimensions.

A special topic of the Journal of Machine Learning Research will be devoted to Large Scale Learning, in the line of the NIPS 2007 and ICML 2008 “Efficient Machine Learning” Workshops, and of the Pascal Challenge on Large Scale Learning (

You are invited to submit your contributions to this special issue. For the sake of a principled and fair evaluation, binary classification algorithms must be assessed on the datasets and along the experimental protocol devised for the Large Scale Learning Challenge. More information about the challenge protocol can be found here:

Important dates

Submission: 5 February 2009 ***NEW***
Decision: 15 March 2009
Final versions: 15 April 2009

Topics of Interest

Topics of interest include:

* Applications to very large scale problems in, e.g., bioinformatics, textcategorization, network data
* Efficient training algorithms, e.g., SVMs solvers
* Learning with a budget, e.g., under strict time or memory constraints.
* Efficient parallelization of machine learning algorithms
* Efficient data structures
* On-line learning algorithms
* Large-scale kernel methods
* Coarse to fine algorithms
* Algorithms making use of new hardware, e.g., GPUs, Xilinx

Submission procedure

Authors are kindly invited to follow the standard JMLR format and submission procedure JMLR submission format, the number of pages is limited to 30. Please include a note stating that your submission is for the special topic on Large Scale Learning.

Guest editors

Soeren Sonnenburg, Fraunhofer Institute FIRST, Berlin, Germany
Vojtech Franc, Fraunhofer Institute FIRST, Berlin, Germany
Elad Yom-Tov, IBM Haifa Research Lab, Haifa, Israel
Michele Sebag, LRI, Orsay, France