[Ml-stat-talks] Fwd: [talks] Colloquium speaker Yuxin Chen, TODAY, 4:30pm

Barbara Engelhardt bee at princeton.edu
Mon Feb 29 09:35:12 EST 2016


Talk of interest today.

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Colloquium Speaker
Yuxin Chen, Stanford University
Monday, February 29, 4:30pm
Computer Science 104- large auditorium

The Power of Nonconvex Paradigms for High-Dimensional Estimation

In various scenarios in the information sciences, one wishes to estimate a
large number of parameters from highly incomplete / imperfect data samples.
A growing body of recent work has demonstrated the effectiveness of convex
relaxation --- in particular, semidefinite programming --- for solving many
problems of this kind. However, the computational cost of such convex
paradigms is often unsatisfactory, which limits applicability to
large-dimensional data. This talk follows another route: by formulating the
problems into nonconvex programs, we attempt to optimize the nonconvex
objectives directly. To illustrate the power of this strategy, we present
two concrete stories. The first involves solving a random quadratic system
of equations, which spans many applications ranging from the century-old
phase retrieval problem to various latent-variable models in machine
learning. The second is about recovering a collection of discrete variables
from noisy pairwise difference measurements, which arises when one wishes
to jointly align multiple images or to retrieve the genome phases from
paired sequencing reads. We propose novel nonconvex paradigms for solving
these two problems. In both cases, the proposed solutions can be
accomplished within linear time, while achieving a statistical accuracy
that is nearly un-improvable.

Yuxin Chen is currently a postdoctoral scholar in the Department of
Statistics at Stanford University, supervised by Prof. Emmanuel Candès. He
received the Ph.D. degree in Electrical Engineering and M.S. in Statistics
from Stanford University, M.S. in Electrical and Computer Engineering from
the University of Texas at Austin, and B.E. in microelectronics from
Tsinghua University. His research interests include high-dimensional
structured estimation, convex and nonconvex optimization, statistical
learning, and information theory.



-- 
Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
http://www.cs.princeton.edu/~bee
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