[Sds-seminars] [Sds-announce] S&DS Talk by Kuikui Liu, 2/16, 4pm, "Spectral Independence: A New Tool to Analyze Markov Chains"

elizavette.torres at yale.edu elizavette.torres at yale.edu
Mon Feb 14 17:00:41 EST 2022


   <https://statistics.yale.edu/> Department of Statistics and Data Science



KUIKUI LIU, University of Washington


Wednesday, February 16, 2022

4:00PM to 5:00PM

Zoom: https://yale.zoom.us/j/97960500577

 <https://homes.cs.washington.edu/~liukui17/> Website

 

 

 

Title: Spectral Independence: A New Tool to Analyze Markov Chains

 

Information and Abstract: 

Sampling from high-dimensional probability distributions is a fundamental
and challenging problem encountered throughout science and engineering. One
of the most popular approaches to tackle such problems is the Markov chain
Monte Carlo (MCMC) paradigm. While MCMC algorithms are often simple to
implement and widely used in practice, analyzing the fidelity of the
generated samples remains a difficult problem.

In this talk, I will describe a new technique called "spectral independence"
that my collaborators and I developed over the last couple of years to
analyze Markov chains. This technique has allowed us to break long-standing
barriers and resolve several decades-old open problems in MCMC theory. Our
work has opened up numerous connections with other areas of computer
science, mathematics, and statistical physics, leading to dozens of new
developments as well as exciting new directions of inquiry. I will then
discuss how these connections have allowed us to "unify" nearly all major
algorithmic paradigms for approximate counting and sampling. Finally, I will
conclude with a wide variety of future directions and open problems at the
frontier of this research.

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/ .

 

 

 

 

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