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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"

50 / 82 papers shown
Title
Optimal Implicit Bias in Linear Regression
Optimal Implicit Bias in Linear Regression
K. N. Varma
Babak Hassibi
36
0
0
20 Jun 2025
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Yuanzhe Hu
Kinshuk Goel
Vlad Killiakov
Yaoqing Yang
61
2
0
06 Jun 2025
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
Arjun Subramonian
Elvis Dohmatob
83
0
0
14 Apr 2025
Comparing regularisation paths of (conjugate) gradient estimators in ridge regression
Laura Hucker
Markus Reiß
Thomas Stark
82
1
0
07 Mar 2025
Rethinking Early Stopping: Refine, Then Calibrate
Rethinking Early Stopping: Refine, Then Calibrate
Eugene Berta
David Holzmüller
Michael I. Jordan
Francis Bach
143
1
0
31 Jan 2025
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model
Kaito Takanami
Takashi Takahashi
Ayaka Sakata
153
1
0
27 Jan 2025
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning
Pierre C. Bellec
Yi Shen
124
13
0
03 Jan 2025
Generalization vs. Specialization under Concept Shift
Generalization vs. Specialization under Concept Shift
Alex Nguyen
David J. Schwab
Vudtiwat Ngampruetikorn
OOD
66
0
0
23 Sep 2024
Risk and cross validation in ridge regression with correlated samples
Risk and cross validation in ridge regression with correlated samples
Alexander B. Atanasov
Jacob A. Zavatone-Veth
Cengiz Pehlevan
105
5
0
08 Aug 2024
Precise analysis of ridge interpolators under heavy correlations -- a
  Random Duality Theory view
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view
Mihailo Stojnic
54
1
0
13 Jun 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Michal Dereziñski
Christopher Musco
Jiaming Yang
123
2
0
09 May 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Yufan Li
Subhabrata Sen
Ben Adlam
MLT
177
1
0
18 Apr 2024
High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile
High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile
Jérémie Bigot
Issa-Mbenard Dabo
Camille Male
97
4
0
29 Mar 2024
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Simon Segert
90
0
0
18 Nov 2023
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
Behrad Moniri
Donghwan Lee
Hamed Hassani
Yan Sun
MLT
102
23
0
11 Oct 2023
Six Lectures on Linearized Neural Networks
Six Lectures on Linearized Neural Networks
Theodor Misiakiewicz
Andrea Montanari
143
13
0
25 Aug 2023
Generalized equivalences between subsampling and ridge regularization
Generalized equivalences between subsampling and ridge regularization
Pratik V. Patil
Jin-Hong Du
91
5
0
29 May 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Bhavya Agrawalla
Krishnakumar Balasubramanian
Promit Ghosal
109
2
0
20 Feb 2023
Gradient flow in the gaussian covariate model: exact solution of
  learning curves and multiple descent structures
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures
Antione Bodin
N. Macris
79
4
0
13 Dec 2022
Asymptotics of the Sketched Pseudoinverse
Asymptotics of the Sketched Pseudoinverse
Daniel LeJeune
Pratik V. Patil
Hamid Javadi
Richard G. Baraniuk
Robert Tibshirani
54
10
0
07 Nov 2022
On double-descent in uncertainty quantification in overparametrized
  models
On double-descent in uncertainty quantification in overparametrized models
Lucas Clarté
Bruno Loureiro
Florent Krzakala
Lenka Zdeborová
UQCV
161
14
0
23 Oct 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian
  Processes
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
Liam Hodgkinson
Christopher van der Heide
Fred Roosta
Michael W. Mahoney
UQCV
81
6
0
14 Oct 2022
A Universal Trade-off Between the Model Size, Test Loss, and Training
  Loss of Linear Predictors
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors
Nikhil Ghosh
M. Belkin
74
7
0
23 Jul 2022
Algorithmic Gaussianization through Sketching: Converting Data into
  Sub-gaussian Random Designs
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs
Michal Derezinski
81
5
0
21 Jun 2022
Gaussian Universality of Perceptrons with Random Labels
Gaussian Universality of Perceptrons with Random Labels
Federica Gerace
Florent Krzakala
Bruno Loureiro
Ludovic Stephan
Lenka Zdeborová
107
24
0
26 May 2022
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
Hong Hu
Yue M. Lu
92
16
0
13 May 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step
  Improves the Representation
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Jimmy Ba
Murat A. Erdogdu
Taiji Suzuki
Zhichao Wang
Denny Wu
Greg Yang
MLT
99
129
0
03 May 2022
Benign Overfitting in Time Series Linear Models with Over-Parameterization
Benign Overfitting in Time Series Linear Models with Over-Parameterization
Shogo H. Nakakita
Masaaki Imaizumi
AI4TS
289
5
0
18 Apr 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different
  Learning Regimes
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes
Elvis Dohmatob
A. Bietti
AAML
85
13
0
22 Mar 2022
More Than a Toy: Random Matrix Models Predict How Real-World Neural
  Representations Generalize
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
Alexander Wei
Wei Hu
Jacob Steinhardt
112
72
0
11 Mar 2022
Fast Rates for Noisy Interpolation Require Rethinking the Effects of
  Inductive Bias
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias
Konstantin Donhauser
Nicolò Ruggeri
Stefan Stojanovic
Fanny Yang
94
22
0
07 Mar 2022
Interpolation and Regularization for Causal Learning
Interpolation and Regularization for Causal Learning
L. C. Vankadara
Luca Rendsburg
U. V. Luxburg
Debarghya Ghoshdastidar
CML
55
1
0
18 Feb 2022
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Yue Sheng
Alnur Ali
122
2
0
20 Jan 2022
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High
  Dimensions
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Mojtaba Sahraee-Ardakan
M. Emami
Parthe Pandit
S. Rangan
A. Fletcher
96
9
0
20 Jan 2022
On generalization bounds for deep networks based on loss surface
  implicit regularization
On generalization bounds for deep networks based on loss surface implicit regularization
Masaaki Imaizumi
Johannes Schmidt-Hieber
ODL
83
3
0
12 Jan 2022
Tight bounds for minimum l1-norm interpolation of noisy data
Tight bounds for minimum l1-norm interpolation of noisy data
Guillaume Wang
Konstantin Donhauser
Fanny Yang
162
20
0
10 Nov 2021
Data splitting improves statistical performance in overparametrized
  regimes
Data splitting improves statistical performance in overparametrized regimes
Nicole Mücke
Enrico Reiss
Jonas Rungenhagen
Markus Klein
58
8
0
21 Oct 2021
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge
  Regression in Linear Models?
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
Dominic Richards
Yan Sun
Patrick Rebeschini
74
3
0
26 Aug 2021
Generalization Error Rates in Kernel Regression: The Crossover from the
  Noiseless to Noisy Regime
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime
Hugo Cui
Bruno Loureiro
Florent Krzakala
Lenka Zdeborová
92
85
0
31 May 2021
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter
  Estimators for Regularized Least Squares Regression Problems
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems
Biqiang Mu
Tianshi Chen
L. Ljung
30
5
0
21 Apr 2021
How rotational invariance of common kernels prevents generalization in
  high dimensions
How rotational invariance of common kernels prevents generalization in high dimensions
Konstantin Donhauser
Mingqi Wu
Fanny Yang
87
24
0
09 Apr 2021
Lower Bounds on the Generalization Error of Nonlinear Learning Models
Lower Bounds on the Generalization Error of Nonlinear Learning Models
Inbar Seroussi
Ofer Zeitouni
71
5
0
26 Mar 2021
On the interplay between data structure and loss function in
  classification problems
On the interplay between data structure and loss function in classification problems
Stéphane dÁscoli
Marylou Gabrié
Levent Sagun
Giulio Biroli
100
17
0
09 Mar 2021
Asymptotics of Ridge Regression in Convolutional Models
Asymptotics of Ridge Regression in Convolutional Models
Mojtaba Sahraee-Ardakan
Tung Mai
Anup B. Rao
Ryan Rossi
S. Rangan
A. Fletcher
MLT
45
2
0
08 Mar 2021
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent
  Theory for Deep Neural Networks
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks
Ryumei Nakada
Masaaki Imaizumi
53
2
0
28 Feb 2021
Learning curves of generic features maps for realistic datasets with a
  teacher-student model
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
116
140
0
16 Feb 2021
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
Fanghui Liu
Xiaolin Huang
Yudong Chen
Johan A. K. Suykens
90
4
0
03 Nov 2020
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang
Hongyang R. Zhang
Sen Wu
Christopher Ré
Weijie J. Su
163
11
0
22 Oct 2020
How Important is the Train-Validation Split in Meta-Learning?
How Important is the Train-Validation Split in Meta-Learning?
Yu Bai
Minshuo Chen
Pan Zhou
T. Zhao
Jason D. Lee
Sham Kakade
Haiquan Wang
Caiming Xiong
96
53
0
12 Oct 2020
What causes the test error? Going beyond bias-variance via ANOVA
What causes the test error? Going beyond bias-variance via ANOVA
Licong Lin
Yan Sun
95
34
0
11 Oct 2020
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