Eigenvectors of some large sample covariance matrix ensembles

16 November 2009

Papers citing "Eigenvectors of some large sample covariance matrix ensembles"

43 / 43 papers shown

Title
End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning Christian Bongiorno Efstratios Manolakis Rosario Nunzio Mantegna 22 0 0 02 Jul 2025
Spectral Analysis of Representational Similarity with Limited Neurons Hyunmo Kang Abdulkadir Canatar SueYeon Chung 173 1 0 27 Feb 2025
A theoretical framework for overfitting in energy-based modeling Giovanni Catania A. Decelle Cyril Furtlehner Beatriz Seoane 199 2 0 31 Jan 2025
Transfer learning via Regularized Linear Discriminant Analysis Hongzhe Zhang Arnab Auddy Hongzhe Lee 88 0 0 05 Jan 2025
On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance Jean Barbier Francesco Camilli Justin Ko Koki Okajima 156 7 0 04 Nov 2024
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view Mihailo Stojnic 67 1 0 13 Jun 2024
$The decomposite $T^{2}$-test when the dimension is large$ The decomposite $T^{2}$ -test when the dimension is large Chia-Hsuan Tsai Ming-Tien Tsai CoGe 33 0 0 03 Mar 2024
The Local Ledoit-Peche Law Van Latimer Benjamin D. Robinson 56 1 0 27 Feb 2023
Correlation matrix of equi-correlated normal population: fluctuation of the largest eigenvalue, scaling of the bulk eigenvalues, and stock market Y. Akama 111 3 0 11 Dec 2022
High-dimensional covariance matrices under dynamic volatility models: asymptotics and shrinkage estimation Yi Ding Xinghua Zheng 23 0 0 18 Nov 2022
Asymptotics of the Sketched Pseudoinverse Daniel LeJeune Pratik V. Patil Hamid Javadi Richard G. Baraniuk Robert Tibshirani 67 10 0 07 Nov 2022
Optimal Eigenvalue Shrinkage in the Semicircle Limit D. Donoho M. J. Feldman 55 5 0 10 Oct 2022
Large covariance matrix estimation via penalized log-det heuristics E. Bernardi M. Farné 57 0 0 11 Sep 2022
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs Michal Derezinski 94 6 0 21 Jun 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes Elvis Dohmatob A. Bietti AAML 112 13 0 22 Mar 2022
An Improvement on the Hotelling $T^2$ Test Using the Ledoit-Wolf Nonlinear Shrinkage Estimator Benjamin D. Robinson Robert Malinas Van Latimer Beth Bjorkman Morrison Alfred Hero 125 1 0 25 Feb 2022
Cleaning large-dimensional covariance matrices for correlated samples Z. Burda A. Jarosz 129 8 0 03 Jul 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui Bruno Loureiro Florent Krzakala Lenka Zdeborová 103 84 0 31 May 2021
High-Dimensional Covariance Shrinkage for Signal Detection Benjamin D. Robinson Robert Malinas Alfred Hero 62 1 0 22 Mar 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 126 142 0 16 Feb 2021
Sparsistent filtering of comovement networks from high-dimensional data A. Chakrabarti A. Chakrabarti 34 0 0 22 Jan 2021
Estimation of Large Financial Covariances: A Cross-Validation Approach Vincent W. C. Tan S. Zohren 99 6 0 10 Dec 2020
A Random Matrix Theory Approach to Damping in Deep Learning Diego Granziol Nicholas P. Baskerville AI4CE ODL 128 2 0 15 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 225 698 0 06 Nov 2020
Asymptotics of Ridge (less) Regression under General Source Condition Dominic Richards Jaouad Mourtada Lorenzo Rosasco 101 73 0 11 Jun 2020
$On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression$ On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression Denny Wu Ji Xu 91 123 0 10 Jun 2020
Regularization in High-Dimensional Regression and Classification via Random Matrix Theory Panagiotis Lolas 89 14 0 30 Mar 2020
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 93 78 0 10 Dec 2019
Spiked separable covariance matrices and principal components Xiucai Ding Fan Yang 99 58 0 29 May 2019
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning Lin Liu Rajarshi Mukherjee J. M. Robins 47 2 0 08 Apr 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 357 752 0 19 Mar 2019
Optimal cleaning for singular values of cross-covariance matrices Florent Benaych-Georges J. Bouchaud M. Potters 152 8 0 16 Jan 2019
A Continuous-Time View of Early Stopping for Least Squares Alnur Ali J. Zico Kolter Robert Tibshirani 133 101 0 23 Oct 2018
On the dimension effect of regularized linear discriminant analysis Cheng-Long Wang Binyan Jiang 75 16 0 09 Oct 2017
High dimensional deformed rectangular matrices with applications in matrix denoising Xiucai Ding 139 47 0 22 Feb 2017
Cross-validation based Nonlinear Shrinkage Daniel Bartz 25 15 0 02 Nov 2016
Cleaning large correlation matrices: tools from random matrix theory J. Bun J. Bouchaud M. Potters 96 270 0 25 Oct 2016
Asymptotic properties of Principal Component Analysis and shrinkage-bias adjustment under the Generalized Spiked Population model Rounak Dey Seunggeun Lee 36 8 0 28 Jul 2016
Eigenvalue distributions of variance components estimators in high-dimensional random effects models Fan Zhou Iain M. Johnstone 76 13 0 08 Jul 2016
Spectrum Estimation from Samples Weihao Kong Gregory Valiant 146 75 0 30 Jan 2016
Numerical Implementation of the QuEST Function Olivier Ledoit Michael Wolf 57 70 0 22 Jan 2016
Estimation of the Global Minimum Variance Portfolio in High Dimensions Taras Bodnar Nestor Parolya W. Schmid 98 87 0 02 Jun 2014
Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model D. Donoho M. Gavish Iain M. Johnstone 277 211 0 04 Nov 2013