[Intelligence Seminar] February 23: Alex Smola, GHC 4303, 3:30, "Fast and Sloppy - Scaling Up Linear Models"
Noah A Smith
nasmith at cs.cmu.edu
Tue Feb 16 17:14:52 EST 2010
February 23, 2010
Host: Carlos Guestrin
For meetings, contact Michelle Martin (michelle324 at cs.cmu.edu).
Title: Fast and Sloppy - Scaling Up Linear Models
Alex Smola, Yahoo Research
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
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.
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