Processing of web scale data sets has proven its worth in a range of applications, from ad-click prediction to large recommender systems. In most cases, learning needs to happen real-time, and the latency allowance for predictions is restrictive. Probabilistic predictions are critical in practice on web applications because optimizing the user experience requires being able to compute the expected utilities of mutually exclusive pieces of content. The quality of the knowledge extracted from the information available is restricted by complexity of the model.

One framework that enables complex modelling of data is probabilistic modelling. However, its applicability to big data is restricted by the difficulties of inference in complex probabilistic models, and by computational constraints.

This workshop will focus on applying probabilistic models to big data. Of interest will be algorithms that allow for inference in probabilistic models for big data such as stochastic variational inference and stochastic Monte Carlo. A particular focus will be on existing applications in big data and future applications that would benefit from such approaches.

This workshop brings together leading academic and industrial researchers in probabilistic modelling and large scale data sets.