Large Scale Machine Learning Workshop Featuring CMU's Graphlab

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50 Third Street

San Francisco, 94103

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MLconf presents:

Join us on Monday, July 9th in San Francisco for a full-day workshop on Large Scale Machine Learning. Featuring CMU's Graphlab and including presentations from Twitter, Pandora, Netflix, Intel Labs, IBM Watson, MapR, and many more. Follow @MLconf for updates, discounts and free tickets!

The Big Learning Workshop is a meeting place for both academia and industry to discuss upcoming challenges of large scale machine learning and solution methods. The workshop will include demos and tutorials showcasing the next generation of the GraphLab framework, as well as lectures and demos from the top technology companies about their applied large scale machine learning solutions.

Event Details


  • 8am - 9am: Registration & Contintental Breakfast
  • 9am - Presentations begin (See agenda below)
  • 5pm - 7pm Networking with hosted bar / appetizers


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TimeSessionTalk title (and length)Speaker 08:00 – 09:00 Reception Reception and continental breakfast 09:00 – 10:30 Morning session GraphLab Version 2 Overview (60 mins) Carlos Guestrin Large scale ML challenges (30 mins) Theodore Willke, Intel Labs 10:30 – 10:50 Break 10:50 – 12:20 Late morning session Bloom: Disorderly Programming for Distributed Systems (30 mins) Joseph Hellerstein, UC Berkeley Schism: Graph Partitioning for Scalable Query Processing on Large OLTP Databases (30 mins) Sam Madden – MIT Visualization and Interactive Data Analysis (30 mins) Jeffrey Heer, Stanford 12:20 – 13:50 Lunch Break 13:40 – 14:55 Afternoon session The Parameter Servrer (30 Mins) Alexander Smola, Yahoo! Labs Vowpal Wabbit for Extremely Fast Machine Learning (15 mins) Lihong Li, Yahoo! Research Cassovary Graph Processing System (15 mins) Pankaj Gupta, Twitter Tera-scale deep learning (15 mins) Quoc Le, Stanford 14:55 – 15:15 Break 15:15 – 17:10 Late afternoon session Identifying densely overlapping clusters in large networks Jure Leskovec, Stanford Large-scale Single-pass k-Means Clustering at Scale (30 mins) Ted Dunning, MapR Technologies Recommendations @Netflix: Big Data, Smart Models & Scalable Systems (15 mins) Xavier Amatriain - Netflix Large scale ML at Pandora (15 mins) Tao Ye, Pandora Internet Radio NIMBLE - A toolkit for the implementation of parallel data mining and machine learning algorithms on Map-Reduce (15 mins) Amol Gothing, IBM Watson Machine learning in One Kings Lane (5 mins) Mohit Singh, One Kings Lane 17:10 – 19:00 Poster/demo session See detailed list below


  • Green Marl graph processing framework – Sungpack Hong, Oracle Labs
  • Machine learning benchmark framework – Nicholas Kolegraff, Accenture
  • TBD -Alexander Gray, Georgia Tech
  • Alpine and MADLib Demo – Steven Hilion, Alpine Data Labs
  • Disk-based Massive Graph Computation – Aapo Kyrola, CMU
  • Titan: A Highly Scalable, Distributed Graph Database - Matthias Broecheler, Aurelius
  • Distributed Active Graph Platform, Andrey Logvinov, Meralabs LLC
  • Health Insights in Real-Time. Adam Sadilek, Andrew Abumoussa, Sean Brennan, Henry Kautz University of Rochester
  • YarcData graph analytics contest, Monte LaBute, YarcData
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