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 day-long 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.