Algorithms & Randomness Center (ARC)
Zongchen Chen
September 14, 2023
Klaus 2447 – 11:00 AM
Title:
Sampling from Graphical Models via Spectral Independence
Abstract:
In many scientific settings we use a statistical model to describe a high-dimensional distribution over many variables. Such models are often represented as a weighted graph encoding the dependencies between different variables and are known as graphical models. Graphical models arise in a wide variety of scientific fields throughout science and engineering.
One fundamental task for graphical models is to generate random samples from the associated distribution. The Markov chain Monte Carlo (MCMC) method is one of the simplest and most popular approaches to tackle such problems. Despite the popularity of graphical models and MCMC algorithms, theoretical guarantees of their performance are not known even for some simple models. I will describe a new tool called "spectral independence" to analyze MCMC algorithms and more importantly to reveal the underlying structure behind such models. I will also discuss how these structural properties can be applied to sampling when MCMC fails and to other statistical problems like parameter learning or model fitting.
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Videos of recent talks are available at:
https://smartech.gatech.edu/handle/1853/46836 and http://arc.gatech.edu/node/121
