[Ml-stat-talks] Fwd: [talks] Colloquium Speaker John Duchi Wed. Feb. 19, 4:30pm

David Blei blei at CS.Princeton.EDU
Wed Feb 12 18:56:25 EST 2014


john duchi is a young star in statistical machine learning.  he is
speaking both in the ORFE colloquium and CS colloquium next week.
information about the CS talk is below. (from my reading of the
titles/abstracts,  it looks like he will be covering some of the same
ground in both talks, but with different perspectives.)


---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Wed, Feb 12, 2014 at 1:37 PM
Subject: [talks] Colloquium Speaker John Duchi Wed. Feb. 19, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>

Computation, Communication, and Privacy Constraints on Learning

John Duchi, University of California, Berkeley

Wednesday, February 19, 4:30pm

Computer Science 105

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

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.

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