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Probabilistic Models for Big Data
Neil D Lawrence · Joaquin Quiñonero-Candela · Tianshi Gao · James Hensman · Zoubin Ghahramani · Max Welling · David Blei · Ralf Herbrich

Mon Dec 09 07:30 AM -- 06:30 PM (PST) @ Harvey's Emerald Bay A
Event URL: https://sites.google.com/site/probabilisticmodelsforbigdata/ »

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.

Author Information

Neil D Lawrence (Amazon)
Joaquin Quiñonero-Candela (Facebook)
Tianshi Gao (Facebook)
James Hensman (PROWLER.io)
Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

Max Welling (University of Amsterdam / Qualcomm AI Research)
David Blei (Columbia University)
Ralf Herbrich (Hasso Plattner Institute)

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