|Speaker||Dr. Alex Smola|
|Organization||Carnegie Mellon University|
|Date||September 20, 2013 12:50 PM|
Randomness is an effective tool to better algorithms for document analysis. Depending on its use it can lead to methods that are either more memory efficient, faster to compute, or work in a lower dimensional space. In this talk I will give an overview over techniques such as locality sensitive hashing, shingles and the min hash, and show their relation to recent developments such as conditionally random sampling, the hash kernel, or fast exponential families sampling.
Dr. Alex Smola studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time he was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996, he received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999, he was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, he worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. From 2004 onwards, he worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA. From 2008 to 2012, he worked at Yahoo Research. In spring of 2012, he moved to Google Research to spend a wonderful year in Mountain View.