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Double descent refers to the phase transition that is exhibited by the generalization error of unregularized learning models when varying the ratio between the number of parameters and the number of training samples.
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 1903
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The implicit regularization of ordinary least squares ensembles
D. LeJeune, H. Javadi, and R. G. Baraniuk · 1910
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Convergence analysis of the randomized Newton method with determinantal sampling
M. Mutný, M. Dereziński, and A. Krause · 1910
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A note on Wilks’ internal scatter
H. Robert van der Vaart · 1965
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The matrix angular central gaussian distribution
Yasuko Chikuse · 1990
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High dimensional limit theorems and matrix decompositions on the stiefel manifold
Yasuko Chikuse · 1991
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Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
ZD Bai, YQ Yin, et al · 1993
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On the empirical distribution of eigenvalues of a class of large dimensional random matrices
Jack W Silverstein and ZD Bai · 1995
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No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
Zhi-Dong Bai, Jack W Silverstein, et al · 1998
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Density estimation on the stiefel manifold
Yasuko Chikuse · 1998
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The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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Singular wishart and multivariate beta distributions
M.S. Srivastava · 2003
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Matrix Mathematics: Theory, Facts, and Formulas
Dennis S. Bernstein · 2011
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On the mean and variance of the generalized inverse of a singular wishart matrix
R. Dennis Cook and Liliana Forzani · 2011
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Eigenvectors of some large sample covariance matrix ensembles
Olivier Ledoit and Sandrine Péché · 2011
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Implementing regularization implicitly via approximate eigenvector computation
M. W. Mahoney and L. Orecchia · 2011
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Regularized Laplacian estimation and fast eigenvector approximation
P. O. Perry and M. W. Mahoney · 2011
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Determinantal Point Processes for Machine Learning
Alex Kulesza and Ben Taskar · 2012
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Approximate computation and implicit regularization for very large-scale data analysis
M. W. Mahoney · 2012
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On bilinear forms based on the resolvent of large random matrices
Walid Hachem, Philippe Loubaton, Jamal Najim, and Pascal Vallet · 2013
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Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow
D. F. Gleich and M. W. Mahoney · 2014
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In search of the real inductive bias: on the role of implicit regularization in deep learning
B. Neyshabur, R. Tomioka, and N. Srebro · 2014
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Fast randomized kernel ridge regression with statistical guarantees
Ahmed El Alaoui and Michael W. Mahoney · 2015
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Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
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Benign overfitting in linear regression
P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler · 2019
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Fast determinantal point processes via distortion-free intermediate sampling
Michał Dereziński · 2019
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Distributed estimation of the inverse Hessian by determinantal averaging
Michał Dereziński and Michael W Mahoney · 2019
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Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney, and Manfred K. Warmuth · 2019
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Bayesian experimental design using regularized determinantal point processes
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P. Ma, M. W. Mahoney, and B. Yu · 2015
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RandNLA: Randomized numerical linear algebra
Petros Drineas and Michael W. Mahoney · 2016
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Lectures on randomized numerical linear algebra
Petros Drineas and Michael W. Mahoney · 2016
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A statistical perspective on randomized sketching for ordinary least-squares
G. Raskutti and M. W. Mahoney · 2016
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Unbiased estimates for linear regression via volume sampling
Michał Dereziński and Manfred K. Warmuth · 2017
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Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro · 2017
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Implicit regularization in deep learning
B. Neyshabur · 2017
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Michał Dereziński, Feynman Liang, and Michael W. Mahoney · 2019
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Correcting the bias in least squares regression with volume-rescaled sampling
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Scaling description of generalization with number of parameters in deep learning
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Surprises in high-dimensional ridgeless least squares interpolation
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Implicit regularization in over-parameterized neural networks
M. Kubo, R. Banno, H. Manabe, and M. Minoji · 2019
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Just interpolate: Kernel “ridgeless” regression can generalize
T. Liang and A. Rakhlin · 2019
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Miles E Lopes, N Benjamin Erichson, and Michael W Mahoney · 2019
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Traditional and heavy-tailed self regularization in neural network models
C. H. Martin and M. W. Mahoney · 2019
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The generalization error of random features regression: Precise asymptotics and double descent curve
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Harmless interpolation of noisy data in regression
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