[Colloquium] Wang/Dissertation Defense/Apr 25, 2018

[Margaret Jaffey via Colloquium]

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Jialei Wang

Date:  Wednesday, April 25, 2018

Time:  2:00 PM

Place:  Ryerson 277

Title: Homogeneous and Coupled Distributed Learning

Abstract:
This thesis explores the theoretical foundations of distributed
machine learning with practical considerations. Distributed learning
systems are able to handle data sets that cannot be processed on a
single machine, and utilize parallel computing resources to speed up
the learning process. However, it also brings unique challenges due to
the characteristics of modern data sets and distributed computing
infrastructure: on one hand, machines are required to being able to
extract meaningful parsimonious structures from the large-scale,
high-dimensional data; on the other hand, the heterogeneity in
distributed data sets enforce us to consider flexible models that are
adaptive to each local machine; last but not least, the overall
effectiveness in distributed learning systems depends on the
efficiency of learning algorithms on multiple resources: computing,
communication, sample and memory, and we need to design algorithms
that balance multiple efficiency constraints.

In this thesis, we considered distributed machine learning under both
homogeneous and coupled setting. In the homogeneous setting, each
machine has access to an independent local data set drawn from the
same source distribution, while in the coupled scenario, the local
data sets might drawn from different distributions and the goal is to
extract the common structure through distributed learning. In this
thesis, we study the trade-offs between sample complexity,
computational cost, communication and memory efficiency in both
settings; we propose novel methods that effectively leverage the
similarity/relatedness structure between machines for several
distributed learning problems, with improved theoretical guarantees.
We also examine the practical performance of the proposed approaches
with existing methods via numerical experiments.

Jialei's advisors are Prof. Janos Simon and Prof. Mladen Kolar

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://www.cs.uchicago.edu/phd/phd_announcements#jialei

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Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)               (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
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