Learning at Scale

Dr. Alexander J. Smola
Researcher, Google
Adjunct Professor, UC Berkeley
Given on: October 4, 2012


In this talk I will give an overview over a number of problems arising when learning at scale. After an overview of problems and systems used in large scale inference, I will discuss strategies for parameter distribution and how they can be used to perform inference in massive graphical models. Subsequently I will discuss methods for accelerating function evaluation. This addresses the issues of scalability both in terms of efficiency and problem size.


Dr. 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. In 1996 he received the Master’s 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 is with the Statistical Machine Learning Program at NICTA as a Principal Researcher and Program Leader.