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Graphical Models for the Internet
Amr Ahmed · Alexander Smola

Mon Dec 12 07:00 AM -- 09:00 AM (PST) @ Andulucia II & III
Event URL: http://alex.smola.org/teaching/nips2011/ »

In this tutorial we give an overview over applications and scalable inference in graphical models for the internet. Structured data analysis has become a key enabling technique to process significant amounts of data, ranging from entity extraction on webpages to sentiment and topic analysis for news articles and comments. Our tutorial covers large scale sampling and optimization methods for Nonparametric Bayesian models such as Latent Dirichlet Allocation, both from a statistics and a systems perspective. Subsequently we give an overview over a range of generative models to elicit sentiment, ideology, time dependence, hierarchical structure, and multilingual similarity from data. We conclude with an overview of recent advances in (semi)supervised information extraction methods based on conditional random fields and related undirected graphical models.

Author Information

Amr Ahmed (Yahoo! Research)

Amr Ahmed is a Research Scientist at Yahoo! Research. He got his M.Sc and PhD from the School of Computer Science at Carnegie Mellon University in 2009 and 2011 respectively. He is interested in graphical models and Bayesian non-parametric statistics with an eye towards building efficient inference algorithms for such models that scale to the size of the data on the internet. On the application side, he is interested in information retrieval over structured sources, social media ( blogs, news stream, twitter), user modeling and personalization.

Alexander Smola (Amazon)

**AWS Machine Learning**

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