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