NIPS 2015 Workshop on Machine Learning Systems (LearningSys)

The popular Big Learning workshop returns this year at NIPS 2015 in Montreal. In this new version, the workshop will focus more on systems designed for efficient machine learning, check it out and submit something here:

Venue: Emerald Bay B, Harveys


Welcome to the webpage for NIPS 2013 workshop on parallel and large-scale machine learning.

Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions.

Database researchers, however, have a history of developing high performance systems that allow concurrent access while providing theoretical guarantees on correctness. In recent years, database systems have been developed specifically to tackle Big Learning tasks.

This workshop aims to bring together the two communities and facilitate the cross-pollination of ideas. Rather than passively using DB systems, ML researchers can apply major DB concepts to their work; DB researchers stand to gain an understanding of the ML challenges and better guide the development of their Big Learning systems.

The goals of the workshop are:

  • Identify challenges faced by ML practitioners in Big Learning setting
  • Showcase recent and ongoing progress towards parallel ML algorithms
  • Highlight recent and significant DB research in addressing Big Learning problems
  • Introduce DB implementations of Big Learning systems, and the principle considerations and concepts underlying their designs

Focal points for discussions and solicited submissions include but are not limited to:

  • Distributed algorithms for online and batch learning
  • Parallel (multicore) algorithms for online and batch learning
  • Theoretical analysis of distributed and parallel learning algorithms
  • Implementation studies of large-scale distributed inference and learning algorithms --- challenges faced and lessons learnt
  • Database systems for Big Learning --- models and algorithms implemented, properties (availability, consistency, scalability, etc.), strengths and limitations

Venue: Lake Tahoe, Nevada

Dates: Monday, December 9th, 2013

Key Dates

The dates below are subject to change.

Call for papers announced:August 25th, 2013
Submission deadline:October 9th, 2013 October 25th, 2013
Author Notification:November 8th, 2013
Final version deadline:November 15th, 2013
Workshop:December 9th, 2013

Latest News

  • December 4th, 2013: Papers updated.
  • November 13th, 2013: Schedule updated.
  • November 8th, 2013: Acceptance notifications emailed.
  • October 14th, 2013: Keynote speakers are confirmed.
  • October 5th, 2013: Submission deadline extended to October 25th.
  • August 25th, 2013: Website is live.
  • August 16th, 2013: Workshop proposal accepted!

.. more ..