Fast and Sloppy - Scaling up Linear Models

Abstract:

In this talk I present an overview over a range of methods designed to
scale up linear models both in terms of model complexity and in terms
of their ability to process large amounts of data. The first aspect is
addressed by hashing feature vectors for both prediction and matrix
factorization. The second aspect can be dealt with by parallelizing
stochastic gradient descent optimization procedures. I will present an
algorithm suitable for multicore parallelism.

Biography:

Dr. Alex Smola is Principal Researcher at Yahoo! Research, Santa Clara
and Adjunct Professor at the Australian National University. Prior to
that until 2008 he was Senior Principal Researcher and Program Leader
at the Statistical Machine Learning Program at NICTA. He received his
Diplom in Physics from the University of Technology in Munich and his
Doctoral degree in Computer Science from the University of Technology
in Berlin. He has worked at AT&T Research, the Fraunhofer Institute,
the Australian National University, NICTA and Yahoo!. His research
interest are nonparametric methods for estimation, in particular
kernel methods and exponential families. This includes Support Vector
Machines, Gaussian processes, and conditional random fields. He is
currently working on large scale methods for document analysis and
representation, such as nonparametric Bayesian models. He has
organized workshops at NIPS, EUROCOLT, ICML and 5 Machine Learning
Summer Schools. Moreover, he served on the senior program committee of
COLT, ICML, NIPS, and AAAI. He has written one book and edited 4
books.