[Ml-stat-talks] Fwd: [talks] Colloquium Speaker: Dimitris Papailiopoulos Monday, March 7- 12:30pm

Barbara Engelhardt bee at princeton.edu
Tue Mar 1 16:57:46 EST 2016

Talk of interest next week.


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Please note: The Large Auditorium does not permit food, therefore lunch
will not be served.

Colloquium Speaker
Dimitris Papailiopoulos , University of California, Berkeley
Monday, March 7, 2016 - 12:30pm
Computer Science Large Auditorium, 104

Less Talking, More Learning: Avoiding Coordination In Parallel Machine
Learning Algorithms

The recent success of machine learning (ML) in both science and industry
has generated an increasing demand to support ML algorithms at scale. In
this talk, I will discuss strategies to gracefully scale machine learning
on modern parallel computational platforms. A common approach to such
scaling is coordination-free parallel algorithms, where individual
processors run independently without communication, thus maximizing the
time they compute. However, analyzing the performance of these algorithms
can be challenging, as they often introduce race conditions and
synchronization problems.

In this talk, I will introduce a general methodology for analyzing
asynchronous parallel algorithms. The key idea is to model the effects of
core asynchrony as noise in the algorithmic input.  This allows us to
understand the performance of several popular asynchronous machine learning
approaches, and to determine when asynchrony effects might overwhelm them.
To overcome these effects, I will propose a new framework for parallelizing
ML algorithms, where all memory conflicts and race conditions can be
completely avoided. I will discuss the implementation of these ideas in
practice, and demonstrate that they outperform the state-of-the-art across
a large number of ML tasks on gigabyte-scale data sets.

Dimitris Papailiopoulos is a postdoctoral researcher in the Department of
Electrical Engineering and Computer Sciences at UC Berkeley and a member of
the AMPLab. His research interests span machine learning, coding theory,
and parallel and distributed algorithms, with a current focus on
coordination-free parallel machine learning, large-scale data and graph
analytics, and the use of codes to speed up distributed computation.
Dimitris completed his Ph.D. in electrical and computer engineering at UT
Austin in 2014. At Austin he worked under the supervision of Alex Dimakis.
In 2015, he received the IEEE Signal Processing Society, Young Author Best
Paper Award.
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