2 papers
math.ST2026
Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation
Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern
Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have drive…
stat.AP2026
Come Together: Analyzing Popular Songs Through Statistical Embeddings
Matthew Esmaili Mallory, Mark Glickman, Jason Brown
Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. Ho…