[Ml-stat-talks] elad hazan talk -- thurs, 4/3, 4:30 -- CS 105

Robert Schapire schapire at CS.Princeton.EDU
Wed Apr 2 16:26:05 EDT 2014

  Sublinear Optimization for Machine Learning
Thursday, April 3, 2014 - 4:30pm to 5:30pm
CS Department Colloquium Series
Computer Science Small Auditorium (Room 105)
Host: Robert Schapire
Elad Hazan (Technion, Israel Institute of Technology)

     In many modern optimization problems, particularly those arising in 
machine learning, the amount data is too large to apply standard convex 
optimization methods. We'll discuss new optimization algorithms that 
make use of randomization to prune the data produce a correct solution 
albeit running in time which is smaller than the data representation, 
i.e. sublinear running time. We'll present such sublinear-time 
algorithms for linear classification, support vector machine training, 
semi-definite programming and other optimization problems.  These new 
algorithms are based on a primal-dual approach, and use a combination of 
novel sampling techniques and the randomized implementation of online 
learning algorithms. We'll describe information-theoretic lower bounds 
that show our running times to be nearly best possible in the unit-cost 
RAM model.

     The talk will be self contained - no prior knowledge in convex 
optimization or machine learning is assumed.

     Elad Hazan is an associate professor of operations research at the 
Technion, Israel Institute of Technology. His main research area is 
machine learning and its relationship to optimization, game theory and 
computational complexity. Elad received his Ph.D. in computer science 
from Princeton University. He is the recipient of several best paper 
awards including the Goldberg Best Paper award (twice), and the European 
Research Council starting grant.

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