[Colloquium] Talks at TTIC: Cho-Jui Hsieh, University of Texas at Austin

[Dawn Ellis]

When:     Thursday, April 16th at 11am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Cho-Jui Hsieh, University of Texas at Austin

Title:       Exploiting Structure in Large-Scale Machine Learning Problems

Abstract:

With an immense growth of data, there is a great need for solving
large-scale machine learning problems. Classical optimization algorithms
usually cannot scale up due to huge amount of data and/or model parameters.
In this talk, I will show that the scalability issues can often be resolved
by exploiting three types of structure in machine learning problems:
problem structure, model structure, and data distribution. This central
idea can be applied to many machine learning problems, including kernel
machines for classification or regression, matrix factorization for
recommender systems, and structure learning for graphical models.

To demonstrate this central idea, I will describe a Newton-like algorithm
for solving the L1-regularized Gaussian maximum likelihood estimator (MLE).
This estimator has strong statistical guarantee in recovering a sparse
inverse covariance, but requires solving a difficult non-smooth
log-determinant program with number of parameters that scale quadratically
with number of random variables. State-of-the-art methods thus cannot
handle more than 20,000 random variables. I will present a Newton-like
algorithm for solving this problem. By exploiting structure of problem,
model, and data distribution, our proposed algorithm can solve 1 million
dimensional L1-regularized Gaussian MLE (which has 1-trillion parameters)
in one day using a single machine.

Short Bio:

Cho-Jui Hsieh is a Ph.D. student at University of Texas at Austin. His
research focus is developing new algorithms and optimization techniques for
large-scale machine learning problems. Cho-Jui obtained his B.S. degree in
2007 and M.S. degree in 2009 from National Taiwan University (advisor:
Chih-Jen Lin). Currently he is a member of Center for Big Data Analytics
led by Inderjit Dhillon. He is the recipient of the IBM Ph.D. fellowship in
2013-2015, the best research paper award in KDD 2010, and the best paper
award in ICDM 2012.

Host:  Greg Shakhnarovich, greg at ttic.edu

-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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