LCCC - NIPS10 Learning on Cores, Clusters and Clouds

LCCC - NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds

Date and Time: 7:30am - 6:30pm Saturday, December 11, 2010

Schedule Time Event Speaker 7:30 - 8:00 Opening remarks and overview of the field Video John Langford 8:00 - 9:00 Keynote: Averaging algorithms and distributed optimization John N. Tsitsiklis 9:00 - 9:20 Coffee Break and Poster Session 9:20 - 9:45 Optimal Distributed Online Prediction Using Mini-Batches Lin Xiao 9:45 - 10:10 MapReduce/Bigtable for Distributed Optimization Slav Petrov 10:10 - 10:30 Mini Talks Part I 10:30 - 15:30 Poster Session and Ski Break 14:00 - 15:30 Unofficial Tutorial on Vowpal Wabbit Video Langford et al. 15:30 - 16:30 Keynote: Machine Learning in the Cloud with GraphLab Carlos Guestrin 16:30 - 16:55 Distributed MAP Inference for Undirected Graphical Models Sameer Singh 16:55 - 17:15 Coffee Break and Poster Session 17:15 - 17:40 Gradient Boosted Decision Trees on Hadoop Jerry Ye 17:40 - 18:00 Mini Talks Part II 18:00 - 18:30 Panel discussion and summary 18:30 Last chance to look at posters

Keynote Speakers John Tsitsiklis Carlos Guestrin

Overview In the current era of web-scale datasets, high throughput biology and astrophysics, and multilanguage machine translation, modern datasets no longer fit on a single computer and traditional machine learning algorithms often have prohibitively long running times. Parallelized and distributed machine learning is no longer a luxury; it has become a necessity. Moreover, industry leaders have already declared that clouds are the future of computing, and new computing platforms such as Microsoft's Azure and Amazon's EC2 are bringing distributed computing to the masses. The machine learning community has been slow to react to these important trends in computing, and it is time for us to step up to the challenge. While some parallel and distributed machine learning algorithms already exist, many relevant issues are yet to be addressed. Distributed learning algorithms should be robust to node failures and network latencies, and they should be able to exploit the power of asynchronous updates. Some of these issues have been tackled in other fields where distributed computation is more mature, such as convex optimization and numerical linear algebra, and we can learn from their successes and their failures. The goals of our workshop are: To draw the attention of machine learning researchers to this rich and emerging area of problems and to establish a community of researchers that are interested in distributed learning. To define a number of common problems for distributed learning (online/batch, synchronous/asynchronous, cloud/cluster/multicore) and to encourage future research that is comparable and compatible To expose the learning community to relevant work in fields such as distributed optimization and distributed linear algebra. To identify research problems that are unique to distributed learning.

Organizers John Langford (Yahoo Research) Ofer Dekel (Microsoft Research) John Duchi (UC Berkeley and Google Research) Alekh Agarwal (UC Berkeley) Lawrence Cayton (MPI Tuebingen)

Program Committee Ron Bekkerman - LinkedIn Misha Bilenko - Microsoft Ran Gilad-Bachrach - Microsoft Guy Lebanon - Georgia Tech Ilan Lobel - NYU Gideon Mann - Google Ryan McDonald - Google Ohad Shamir - Microsoft Alex Smola - Yahoo! S V N Vishwanathan - Purdue Martin Wainwright - UC Berkeley Lin Xiao - Microsoft