Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1507.03003
Cited By
v1
v2 (latest)
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
10 July 2015
Yan Sun
Stefan Wager
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification"
50 / 82 papers shown
Title
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
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
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
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
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
Pierre C. Bellec
Yi Shen
124
13
0
03 Jan 2025
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
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
Mihailo Stojnic
54
1
0
13 Jun 2024
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
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
Jérémie Bigot
Issa-Mbenard Dabo
Camille Male
97
4
0
29 Mar 2024
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
Behrad Moniri
Donghwan Lee
Hamed Hassani
Yan Sun
MLT
102
23
0
11 Oct 2023
Six Lectures on Linearized Neural Networks
Theodor Misiakiewicz
Andrea Montanari
143
13
0
25 Aug 2023
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
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
Antione Bodin
N. Macris
79
4
0
13 Dec 2022
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
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
Liam Hodgkinson
Christopher van der Heide
Fred Roosta
Michael W. Mahoney
UQCV
83
6
0
14 Oct 2022
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
Michal Derezinski
81
5
0
21 Jun 2022
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
Hong Hu
Yue M. Lu
92
16
0
13 May 2022
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
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
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
Alexander Wei
Wei Hu
Jacob Steinhardt
112
72
0
11 Mar 2022
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
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
Yue Sheng
Alnur Ali
122
2
0
20 Jan 2022
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
Masaaki Imaizumi
Johannes Schmidt-Hieber
ODL
83
3
0
12 Jan 2022
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
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?
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
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
Biqiang Mu
Tianshi Chen
L. Ljung
32
5
0
21 Apr 2021
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
Inbar Seroussi
Ofer Zeitouni
71
5
0
26 Mar 2021
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
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
Ryumei Nakada
Masaaki Imaizumi
55
2
0
28 Feb 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
116
140
0
16 Feb 2021
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
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?
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
Licong Lin
Yan Sun
95
34
0
11 Oct 2020
1
2
Next