Learning on Cores, Clusters, and Clouds

Learning on Cores, Clusters, and Clouds

16 Lectures · Dec 10, 2010

About

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 workshop aims 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. We would like 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. We also hope to expose the learning community to relevant work in fields such as distributed optimization and distributed linear algebra. The daylong workshop aims to identify research problems that are unique to distributed learning. The target audience includes leading researchers from academia and industry that are interested in distributed and large-scale learning.

Workshop homepage: http://lccc.eecs.berkeley.edu/

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Uploaded videos:

Welcome Address

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22:09

Opening remarks on the Workshop Learning on Cores, Clusters, and Clouds

John Langford

Jan 13, 2011

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3780 Views

Opening

Keynote Speakers

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01:04:31

Averaging algorithms and distributed optimization

John N. Tsitsiklis

Jan 13, 2011

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7966 Views

Keynote
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54:26

Machine Learning in the Cloud with GraphLab

Carlos Guestrin

Jan 13, 2011

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9017 Views

Keynote

Tutorial

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01:09:48

Vowpal Wabbit

John Langford,

Daniel Hsu,

Nikos Karampatziakis,

Matt Hoffman

Jan 13, 2011

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19662 Views

Tutorial

Lectures

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21:21

Optimal Distributed Online Prediction Using Mini-Batches

Lin Xiao

Jan 13, 2011

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4634 Views

Lecture
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23:33

MapReduce/Bigtable for Distributed Optimization

Slav Petrov

Jan 13, 2011

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6657 Views

Lecture
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23:35

Distributed MAP Inference for Undirected Graphical Models

Sameer Singh

Jan 13, 2011

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4814 Views

Lecture
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23:12

Gradient Boosted Decision Trees on Hadoop

Jerry Ye

Jan 13, 2011

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24163 Views

Lecture

Mini Talks

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04:15

Building Heterogeneous Platforms for End-to-end Online Learning Based on Dataflo...

Benoit Corda

Jan 13, 2011

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3810 Views

Poster
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05:07

A Convenient Framework for Efficient Parallel Multipass Algorithms

Markus Weimer

Jan 13, 2011

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3671 Views

Poster
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04:08

Parallel Online Learning

Nikos Karampatziakis

Jan 13, 2011

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3436 Views

Poster
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04:39

The Learning Behind the Gmail Priority Inbox

Douglas Aberdeen

Jan 13, 2011

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5591 Views

Poster
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05:14

Learning to Rank on a Cluster using Boosted Decision Trees

Krysta M. Svore

Jan 13, 2011

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10166 Views

Poster
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04:43

Parallel Splash Gibbs Sampling

Joseph Gonzalez

Jan 13, 2011

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3994 Views

Poster
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04:44

Distributed Markov chain Monte Carlo

Lawrence Murray

Jan 13, 2011

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4081 Views

Poster
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03:36

All-Pairs Nearest Neighbor Search on Manycore Systems

Lawrence Cayton

Jan 13, 2011

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3259 Views

Poster