[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Alekh Agarwal TuesFeb 28, 4:30pm
blei at CS.Princeton.EDU
Wed Feb 22 21:23:41 EST 2012
next tuesday 2/28 at 4:30pm in CS 105: computation meets statistics.
come to the tea room at 4pm for tea and snacks.
---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Wed, Feb 22, 2012 at 5:05 PM
Subject: [talks] Colloquium Speaker Alekh Agarwal TuesFeb 28, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>
Computation meet Statistics: Trade-offs and fundamental limits
Alekh Agarwal, University of California, Berkeley
Tuesday, February 28, 2012, 4:30 PM
Computer Science Small Auditorium (Room 105)
The past decade has seen the emergence of datasets of an
unprecedented scale, with both large sample sizes and dimensionality.
Massive data sets arise in various domains, among them computer
vision, natural language processing, computational biology, social
networks analysis and recommendation systems, to name a few. In many
such problems, the bottleneck is not just the number of data samples,
but also the computational resources available to process the data.
Thus, a fundamental goal in these problems is to characterize how
estimation error behaves as a function of the sample size, number of
parameters, and the computational budget available.
In this talk, I present two research threads that provide
complementary lines of attack on this broader research agenda: lower
bounds for statistical estimation with computational constraints, and
(ii) distributed algorithms for statistical inference. The first
characterizes fundamental limits in a uniform sense over all methods,
whereas the latter provides explicit algorithms that exploits the
interaction of computational and statistical considerations in a
distributed computing environment.
[Joint work with John Duchi, Pradeep Ravikumar, Peter Bartlett and
Alekh Agarwal is a fifth year PhD student at UC Berkeley, jointly
advised by Peter Bartlett and Martin Wainwright. Alekh has received
PhD fellowships from Microsoft Research and Google. His main research
interests are in the areas of machine learning, convex optimization,
high-dimensional statistics, distributed machine learning and
understanding the computational trade-offs in machine learning
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