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Pedregoza, F., Paquette, C., Trogdon, T. and Pennington, J. (n.d.) (2024) Random Matrix Theory and Machine Learning Tutorial [PowerPoint Slides].
https://random-matrix-learning.github.io/#presentation1

has been cited by the following article:

• TITLE: Denoising Data with Random Matrix Theory

  AUTHORS: Nathan Jiang

  KEYWORDS: Random Matrix Theory, Universality, Wishart Matrices, Marchenko-Pastur (M-P) Distribution, Noise, Sparsity, Signaling, Linear Sketching

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.11, November 27, 2024

  ABSTRACT: Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries, the introduction of noise, and the size of the matrix, the resulting eigenvalue and eigenvector distributions remain relatively unchanged. However, when there are certain anomalous eigenvalues and their corresponding eigenvectors that do not follow the predicted distributions, it could indicate that there’s an underlying non-random signal inside the data. As data and matrices become more important in the sciences and computing, so too will the importance of processing them with the principles of random matrix theory.