Datasets with millions of observations can be gathered by crawling the web, mining business databases, or connecting a cheap video tuner to a laptop. Vastly more ambitious learning systems are theoretically possible. The literature shows no shortage of ideas for sophisticated statistical models. The computational cost of learning algorithms is now the bottleneck. During the last decade, dataset size has outgrown processor speed. Meanwhile, machine learning algorithms became more principled, and also more computationally expensive.

The workshop investigates computationally efficient ways to exploit such large datasets using kernel machines. It will show how adequately designed kernel machines can efficiently process millions of examples. It will also debate whether kernel machines are the best way to achieve such objectives.

With this workshop, we hope to raise the awareness of the community about the opportunities and challenges offered by large scale datasets. The target audience includes people who are willing to take advantage of the applicative opportunities offered by large datasets, as well as people who are simply curious of the latest advances in this area.

Topics to be discussed: