The goal of this workshop is to bring together researchers from both industry and academia to share their experiences of implementing large-scale applications of online learning and online decision-making. A selection of example applications includes banner advertisement selection, news story selection, targeted email, and recommender systems. The workshop will focus on the scalability of current online methods to large-scale implementations that are of practical value to industry. Relevant methods include exploration/exploitation trade-offs (e.g. contextual bandits), large-scale gradient descent, parallelization, collaborative filtering, unsupervised feature learning and dimensionality reduction.

For more information visit the Workshop website.