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High-Dimensional Asymptotics of Prediction: Ridge Regression and
  Classification
v1v2 (latest)

High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

10 July 2015
Yan Sun
Stefan Wager
ArXiv (abs)PDFHTML

Papers citing "High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification"

32 / 82 papers shown
Title
Out-of-sample error estimate for robust M-estimators with convex penalty
Out-of-sample error estimate for robust M-estimators with convex penalty
Pierre C. Bellec
129
17
0
26 Aug 2020
Revisiting minimum description length complexity in overparameterized
  models
Revisiting minimum description length complexity in overparameterized models
Raaz Dwivedi
Chandan Singh
Bin Yu
Martin J. Wainwright
72
5
0
17 Jun 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot
Berfin cSimcsek
Francesco Spadaro
Clément Hongler
Franck Gabriel
80
68
0
17 Jun 2020
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in
  High Dimensions
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri
Ramtin Pedarsani
Christos Thrampoulidis
84
29
0
16 Jun 2020
Asymptotics of Ridge (less) Regression under General Source Condition
Asymptotics of Ridge (less) Regression under General Source Condition
Dominic Richards
Jaouad Mourtada
Lorenzo Rosasco
88
73
0
11 Jun 2020
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized
  Linear Regression
On the Optimal Weighted ℓ2\ell_2ℓ2​ Regularization in Overparameterized Linear Regression
Denny Wu
Ji Xu
75
123
0
10 Jun 2020
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian
  Kernel, a Precise Phase Transition, and the Corresponding Double Descent
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent
Zhenyu Liao
Romain Couillet
Michael W. Mahoney
91
93
0
09 Jun 2020
Spectra of the Conjugate Kernel and Neural Tangent Kernel for
  linear-width neural networks
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Z. Fan
Zhichao Wang
115
74
0
25 May 2020
Fast cross-validation for multi-penalty ridge regression
Fast cross-validation for multi-penalty ridge regression
M. A. van de Wiel
M. V. Nee
A. Rauschenberger
34
3
0
19 May 2020
Finite-sample Analysis of Interpolating Linear Classifiers in the
  Overparameterized Regime
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri S. Chatterji
Philip M. Long
95
109
0
25 Apr 2020
Regularization in High-Dimensional Regression and Classification via
  Random Matrix Theory
Regularization in High-Dimensional Regression and Classification via Random Matrix Theory
Panagiotis Lolas
84
14
0
30 Mar 2020
Rethinking Parameter Counting in Deep Models: Effective Dimensionality
  Revisited
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Wesley J. Maddox
Gregory W. Benton
A. Wilson
136
61
0
04 Mar 2020
Optimal Regularization Can Mitigate Double Descent
Optimal Regularization Can Mitigate Double Descent
Preetum Nakkiran
Prayaag Venkat
Sham Kakade
Tengyu Ma
85
133
0
04 Mar 2020
The role of regularization in classification of high-dimensional noisy
  Gaussian mixture
The role of regularization in classification of high-dimensional noisy Gaussian mixture
Francesca Mignacco
Florent Krzakala
Yue M. Lu
Lenka Zdeborová
58
90
0
26 Feb 2020
Implicit Regularization of Random Feature Models
Implicit Regularization of Random Feature Models
Arthur Jacot
Berfin Simsek
Francesco Spadaro
Clément Hongler
Franck Gabriel
85
83
0
19 Feb 2020
Exact expressions for double descent and implicit regularization via
  surrogate random design
Exact expressions for double descent and implicit regularization via surrogate random design
Michal Derezinski
Feynman T. Liang
Michael W. Mahoney
82
78
0
10 Dec 2019
The Implicit Regularization of Ordinary Least Squares Ensembles
The Implicit Regularization of Ordinary Least Squares Ensembles
Daniel LeJeune
Hamid Javadi
Richard G. Baraniuk
143
43
0
10 Oct 2019
Ridge Regression: Structure, Cross-Validation, and Sketching
Ridge Regression: Structure, Cross-Validation, and Sketching
Sifan Liu
Yan Sun
CML
115
48
0
06 Oct 2019
Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting
Marc Lelarge
Léo Miolane
71
29
0
08 Jul 2019
WONDER: Weighted one-shot distributed ridge regression in high
  dimensions
WONDER: Weighted one-shot distributed ridge regression in high dimensions
Yan Sun
Yueqi Sheng
OffRL
92
51
0
22 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Trevor Hastie
Andrea Montanari
Saharon Rosset
Robert Tibshirani
305
747
0
19 Mar 2019
A Continuous-Time View of Early Stopping for Least Squares
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali
J. Zico Kolter
Robert Tibshirani
104
97
0
23 Oct 2018
Optimal Covariance Estimation for Condition Number Loss in the Spiked
  Model
Optimal Covariance Estimation for Condition Number Loss in the Spiked Model
D. Donoho
Behrooz Ghorbani
141
7
0
17 Oct 2018
Asymptotics for Sketching in Least Squares Regression
Asymptotics for Sketching in Least Squares Regression
Yan Sun
Sifan Liu
61
13
0
14 Oct 2018
Adapting to Unknown Noise Distribution in Matrix Denoising
Adapting to Unknown Noise Distribution in Matrix Denoising
Andrea Montanari
Feng Ruan
Jun Yan
100
13
0
06 Oct 2018
Distributed linear regression by averaging
Distributed linear regression by averaging
Yan Sun
Yueqi Sheng
FedML
94
66
0
30 Sep 2018
Optimal ridge penalty for real-world high-dimensional data can be zero
  or negative due to the implicit ridge regularization
Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
D. Kobak
Jonathan Lomond
Benoit Sanchez
94
89
0
28 May 2018
On the dimension effect of regularized linear discriminant analysis
On the dimension effect of regularized linear discriminant analysis
Cheng-Long Wang
Binyan Jiang
51
15
0
09 Oct 2017
Permutation methods for factor analysis and PCA
Permutation methods for factor analysis and PCA
Yan Sun
82
54
0
02 Oct 2017
Optimal prediction in the linearly transformed spiked model
Optimal prediction in the linearly transformed spiked model
Edgar Dobriban
W. Leeb
A. Singer
83
20
0
07 Sep 2017
High-dimensional regression adjustments in randomized experiments
High-dimensional regression adjustments in randomized experiments
Stefan Wager
Wenfei Du
Jonathan E. Taylor
Robert Tibshirani
299
117
0
22 Jul 2016
Prediction risk for the horseshoe regression
Prediction risk for the horseshoe regression
A. Bhadra
J. Datta
Yunfan Li
Nicholas G. Polson
Brandon T. Willard
173
16
0
16 May 2016
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