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We study risk of the minimum norm linear least squares estimator in when the number of parameters $d$ depends on $n$, and $\frac{d}{n} \rightarrow \infty$.
On best approximate solutions of linear matrix equations
Roger Penrose · 1956
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On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
Yong-Quan Yin, Zhi-Dong Bai, and Pathak R Krishnaiah · 1988
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Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
Zhi-Dong Bai and Yong-Qua Yin · 1993
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On the distribution of the largest eigenvalue in principal components analysis
Iain M. Johnstone · 2001
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Eigenvalues of large sample covariance matrices of spiked population models
Jinho Baik and Jack W. Silverstein · 2005
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Geometric representation of high dimension, low sample size data
Peter Hall, James Stephen Marron, and Amnon Neeman · 2005
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The high-dimension, low-sample-size geometric representation holds under mild conditions
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Pca consistency in high dimension, low sample size context
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Surprising asymptotic conical structure in critical sample eigen-directions
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Asymptotics of empirical eigenstructure for high dimensional spiked covariance
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To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
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Just interpolate: Kernel ”ridgeless” regression can generalize
Alexander Rakhlin Tengyuan Liang · 2018
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Benign overfitting in linear regression
Peter L. Bartlett, Philip M. Long, Gabor Lugosi, and Alexander Tsigler · 2019
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Two models of double descent for weak features
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Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
Vladimir Koltchinskii and Karim Lounici · 2016
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A general framework for consistency of principal component analysis
Dan Shen, Haipeng Shen, and J. S. Marron
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The statistics and mathematics of high dimension low sample size asymptotics
Dan Shen, Haipeng Shen, Hongtu Zhu, and J. S. Marron
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor J. Hastie, Andrea Montanari, Saharon Rosset, and Ryan J. Tibshirani · 2019
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