<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000'><h1 class="page__title title" id="page-title">Computation, Communication, and Privacy Constraints on Learning</h1><p><span class="event-speaker">
<a href="http://www.cs.berkeley.edu/%7Ejduchi/">John Duchi</a>, </span><span class="event-speaker-from"><a href="http://www.eecs.berkeley.edu/">University of California, Berkeley<br></a></span></p><p>Wednesday, February 19, 4:30pm</p><p>Computer Science 105<br></p><p><br></p><p>How can we maximally leverage available resources--such as
computation, communication, multi-processors, or even privacy--when
performing machine learning? In this talk, I will suggest statistical
risk (a rigorous notion of the accuracy of learning procedures) as a way
to incorporate such criteria in a framework for development of
algorithms. In particular, we follow a two-pronged approach, where we
(1) study the fundamental difficulties of problems, bringing in tools
from optimization, information theory, and statistical minimax theory,
and (2) develop algorithms that optimally trade among multiple criteria
for improved performance. The resulting algorithms are widely applied in
industrial and academic settings, giving up to order of magnitude
improvements in speed and accuracy for several problems. To illustrate
the practical benefits that a focus on the tradeoffs of statistical
learning procedures brings, we explore examples from computer vision,
speech recognition, document classification, and web search.</p><p><br></p>
<div>John is currently a PhD candidate in computer science at Berkeley,
where he started in the fall of 2008. His research interests include
optimization, statistics, machine learning, and computation. He works in
the Statistical Artificial Intelligence Lab (SAIL) under the joint
supervision of Michael Jordan and Martin Wainwright. He obtained his MA
in statistics in Fall 2012, and received a BS and MS from Stanford
University in computer science under the supervision of Daphne Koller.
John has won several awards and fellowships, including a best student
paper award at the International Conference on Machine Learning (ICML)
and the NDSEG and Facebook graduate fellowships. John has also worked as
a software engineer and researcher at Google Research under the
supervision of Yoram Singer and Fernando Pereira.</div>
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