Limit Theory for the largest eigenvalues of sample covariance matrices
with heavy-tails

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Limit Theory for the largest eigenvalues of sample covariance matrices with heavy-tails

27 August 2011

Richard A. Davis

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Papers citing "Limit Theory for the largest eigenvalues of sample covariance matrices with heavy-tails"

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Title
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks Xuanzhe Xiao Zengyi Li Chuanlong Xie Fengwei Zhou 101 3 0 06 Apr 2023
Almost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices Johannes Heiny T. Mikosch 62 22 0 30 Jan 2020
Large sample autocovariance matrices of linear processes with heavy tails Johannes Heiny T. Mikosch 70 6 0 14 Jan 2020
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks Charles H. Martin Michael W. Mahoney 97 56 0 24 Jan 2019
Traditional and Heavy-Tailed Self Regularization in Neural Network Models Charles H. Martin Michael W. Mahoney 108 131 0 24 Jan 2019
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin Michael W. Mahoney AI4CE 141 208 0 02 Oct 2018
Eigenvalues and eigenvectors of heavy-tailed sample covariance matrices with general growth rates: the iid case Johannes Heiny T. Mikosch 76 24 0 24 Aug 2016
Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series Richard A. Davis Johannes Heiny T. Mikosch Xiao-Yi Xie 60 25 0 26 Apr 2016
On the spectral norm of large heavy-tailed random matrices with strongly dependent rows and columns Oliver Pfaffel 53 0 0 30 Nov 2012
Eigenvalues of sample covariance matrices of non-linear processes with infinite variance Richard A. Davis Oliver Pfaffel 75 0 0 26 Nov 2012