NIPS 2013 Workshop on Probabilistic Models for Big Data
December 9th or 10th, 2013 Lake Tahoe, USA.

Website: https://sites.google.com/site/probabilisticmodelsforbigdata/
Email: probabilistic.big.data@gmail.com


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Important Dates:

Submission deadline: October 9th, 2013
Acceptance notification: October 23rd, 2013


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Overview:

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. Topics of interest (non-exhaustive) include:
- probabilistic modelling
- approximate inference
- practical approaches to large scale inference
- applications in big data with probabilistic ingredients


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Confirmed Speakers:

David Blei
Zoubin Ghahramani
Ralf Herbrich
Max Welling
Joaquin Quiñonero Candela


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Submission:

Submissions must be in the NIPS 2013 format (style files available at http://nips.cc/PaperInformation/StyleFiles), with a maximum of 8 pages (excluding one additional page of references, shorter submissions are also welcome). Please submit your contributions here: https://www.easychair.org/conferences/?conf=pmbd2013.


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Organizers:

Neil Lawrence (University of Sheffield)
Joaquin Quiñonero Candela (Facebook)
Tianshi Gao (Facebook)
James Hensman (University of Sheffield)