[Statlist] ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Zhou Fan, Yale University

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Empirical Bayes and Approximate Message Passing algorithms for PCA in high dimensions"  
by Zhou Fan, Yale University


Time: Friday, 4 December 2020, 15:00-​16:00
Place: Zoom at https://ethz.zoom.us/j/92367940258

Abstract: This talk will be divided into two halves. In a first more applied half, I will describe a new empirical Bayes procedure for principal components analysis in high dimensions, which aims to learn a prior distribution for the PCs from the observed data. Its ideas are based around the Kiefer-​Wolfowitz NPMLE, some basic results in asymptotic random matrix theory, and Approximate Message Passing (AMP) algorithms for Bayesian inference. I will explain the interplay between these ideas and demonstrate the method on several genetics examples. In a second more theoretical half, motivated by this application, I will then describe a general extension of AMP algorithms to a class of rotationally invariant matrices. The usual bias correction and state evolution in AMP are replaced by forms involving the free cumulants of the spectral law. I hope to explain the main ideas behind this algorithm, and connect this back to the PCA application. This is joint work with Xinyi Zhong and Chang Su.
Best wishes,

M. Azadkia, Y. Chen, M. Löffler, A. Taeb

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html