Probabilistic Models for Big Data is a NIPS 2013 workshop focussed on processing web-scale data sets with probabilistic methods.
Processing of web scale data sets has proven its worth in a range of applications, from ad-click prediction to large recommender systems. However, the quality of the knowledge extracted from the information available is restricted by complexity of the model: a simple model can only learn simple things, more complex inferences require more complex models.
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.
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.
Accepted invited speakers:
Max Welling, David Blei, Zoubin Ghahramani, Ralf Herbrich and Yoram Singer.