Reading Group

Fall 2025: High-Dimensional Statistics and Asymptotic Theory

Details

• Schedule: Mondays, 6:30–8:30 PM EST (October 20 – December 15)

• Room: To be announced

• Schedule & Papers: View Reading Schedule

Overview

This reading group focuses on recent developments in high-dimensional statistics and asymptotic theory. We explore fundamental concepts, theoretical advances, and applications in modern data science.

References

Below is the list of papers we will be reading in the reading group.

• On the distribution of the largest eigenvalue in principal components analysis

• The dynamics of message passing on dense graphs, with applications to compressed sensing

• The LASSO risk for Gaussian matrices

• Precise error analysis of regularized M estimators in high dimensions

• On the impact of predictor geometry on the performance in high-dimensional settings

• High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

• The Likelihood Ratio Test in High-Dimensional Logistic Regression is Asymptotically a Rescaled Chi-Square

• Observable adjustments in single-index models for regularized M-estimators with bounded p/n