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FastEx: Fast Clustering with Exponential Families
Amr Ahmed · Sujith Ravi · Shravan M Narayanamurthy · Alexander Smola

Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
Clustering is a key component in data analysis toolbox. Despite its importance, scalable algorithms often eschew rich statistical models in favor of simpler descriptions such as $k$-means clustering. In this paper we present a sampler, capable of estimating mixtures of exponential families. At its heart lies a novel proposal distribution using random projections to achieve high throughput in generating proposals, which is crucial for clustering models with large numbers of clusters.

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.

Sujith Ravi (Google Research)
Shravan M Narayanamurthy (Yahoo)
Alexander Smola (Amazon)

**AWS Machine Learning**

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