[Faculty] Fwd: [CSRC.COLLOQUIUM] "Large-Scale Approximate Inference: Theory, Algorithms, and Applications

[Jose Castillo]

[image: SDSU_CSRC Logo.jpg]


DATE:
*Friday, October 21, 2022*


TITLE:
*Large-Scale Approximate Inference: Theory, Algorithms, and Applications*




TIME:
*3:30-4:30PM*



LOCATION:
*In Person - GMCS 314*


SPEAKER/BIO:
*Duy Nguyen, Electrical and Computer Engineering, San Diego State
University *


ABSTRACT:
A central task in statistical inference is the evaluation of the posterior
distribution p(x|y) of the latent variables x given the observed data
variables y. For many models of practical interest, it will be infeasible
to evaluate the posterior distribution, because the dimensionality of the
latent space is too high to work with directly or because the posterior
distribution has a highly complex form. In these situations, we need to
resort to approximate schemes. In this talk, I will review a range of
deterministic approximation schemes, including approximate message passing
(AMP) and variational Bayes (VB) inference, which scale well to large
applications. These are based on analytical approximations to the posterior
distribution, for example by assuming that it factorizes in a particular
way. I will present the theory behind AMP and VB and some related
computationally efficient algorithms in these schemes. Finally, I will
present the applications of approximate inference in (sparse) linear
regression,logistic regression, probit regression, compressed sensing, and
MIMO signal estimation.


Bio: Duy H. N. Nguyen(Senior Member, IEEE) is an Associate Professor in the
Department of Electrical and Computer Engineering, San Diego State
University. He received the B.Eng. from Swinburne University of Technology,
Hawthorn, VIC, Australia in 2005, the M.Sc.from University of Saskatchewan,
Saskatoon, SK, Canada in 2009 and the Ph.D.from McGill University,
Montreal, QC, Canada in 2013. He was a postdoctoral research fellow at
INRS-EMT (University of Quebec), The University of Houston,and the
University of Texas at Austin. He joined SDSU in 2016. His current research
interests include resource allocation in wireless networks, signal
processing for communications, optimization, game theory and machine
learning. He recently received the 2022 NSF CAREER award.


Host:
*Sunil Kumar*

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.





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