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An Equivalence Principle for the Spectrum of Random Inner-Product Kernel
  Matrices with Polynomial Scalings
v1v2 (latest)

An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

The Annals of Applied Probability (Ann. Appl. Probab.), 2022
12 May 2022
Yue M. Lu
H. Yau
ArXiv (abs)PDFHTML

Papers citing "An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings"

20 / 20 papers shown
Kernel ridge regression under power-law data: spectrum and generalization
Kernel ridge regression under power-law data: spectrum and generalization
Arie Wortsman
Bruno Loureiro
148
1
0
06 Oct 2025
Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling
Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling
Lucas Benigni
Elliot Paquette
96
2
0
27 Aug 2025
Models of Heavy-Tailed Mechanistic Universality
Models of Heavy-Tailed Mechanistic Universality
Liam Hodgkinson
Zhichao Wang
Michael W. Mahoney
270
3
0
04 Jun 2025
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions
P. Abry
G. Didier
Oliver Orejola
H. Wendt
295
0
0
30 Jan 2025
A Random Matrix Theory Perspective on the Spectrum of Learned Features
  and Asymptotic Generalization Capabilities
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization CapabilitiesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Yatin Dandi
Luca Pesce
Hugo Cui
Florent Krzakala
Yue M. Lu
Bruno Loureiro
MLT
322
9
0
24 Oct 2024
Extremal Eigenvalues of Random Kernel Matrices with Polynomial Scaling
Extremal Eigenvalues of Random Kernel Matrices with Polynomial Scaling
David Kogan
Sagnik Nandy
Jiaoyang Huang
183
2
0
23 Oct 2024
Spectral Properties of Elementwise-Transformed Spiked Matrices
Spectral Properties of Elementwise-Transformed Spiked MatricesSIAM Journal on Mathematics of Data Science (SIMODS), 2023
Michael J. Feldman
395
6
0
03 Nov 2023
Universality for the global spectrum of random inner-product kernel
  matrices in the polynomial regime
Universality for the global spectrum of random inner-product kernel matrices in the polynomial regime
S. Dubova
Yue M. Lu
Benjamin McKenna
H. Yau
244
10
0
27 Oct 2023
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear ModelsNeural Information Processing Systems (NeurIPS), 2023
David X. Wu
A. Sahai
360
4
0
23 Jun 2023
Spectral clustering in the Gaussian mixture block model
Spectral clustering in the Gaussian mixture block model
Shuangping Li
T. Schramm
307
11
0
29 Apr 2023
Universality laws for Gaussian mixtures in generalized linear models
Universality laws for Gaussian mixtures in generalized linear modelsNeural Information Processing Systems (NeurIPS), 2023
Yatin Dandi
Ludovic Stephan
Florent Krzakala
Bruno Loureiro
Lenka Zdeborová
FedML
249
30
0
17 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
207
4
0
13 Dec 2022
Overparameterized random feature regression with nearly orthogonal data
Overparameterized random feature regression with nearly orthogonal dataInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Zhichao Wang
Yizhe Zhu
332
8
0
11 Nov 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian
  Processes
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2022
Liam Hodgkinson
Christopher van der Heide
Fred Roosta
Michael W. Mahoney
UQCV
256
7
0
14 Oct 2022
Local and global expansion in random geometric graphs
Local and global expansion in random geometric graphsSymposium on the Theory of Computing (STOC), 2022
Siqi Liu
Sidhanth Mohanty
T. Schramm
E. Yang
213
7
0
01 Oct 2022
Optimal Activation Functions for the Random Features Regression Model
Optimal Activation Functions for the Random Features Regression ModelInternational Conference on Learning Representations (ICLR), 2022
Jianxin Wang
José Bento
257
4
0
31 May 2022
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product
  Kernel Regression
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel RegressionJournal of Statistical Mechanics: Theory and Experiment (JSTAT), 2022
Lechao Xiao
Hong Hu
Theodor Misiakiewicz
Yue M. Lu
Jeffrey Pennington
325
21
0
30 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
183
18
0
13 May 2022
The Eigenlearning Framework: A Conservation Law Perspective on Kernel
  Regression and Wide Neural Networks
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks
James B. Simon
Madeline Dickens
Dhruva Karkada
M. DeWeese
511
28
0
08 Oct 2021
Double-descent curves in neural networks: a new perspective using
  Gaussian processes
Double-descent curves in neural networks: a new perspective using Gaussian processesAAAI Conference on Artificial Intelligence (AAAI), 2021
Ouns El Harzli
Bernardo Cuenca Grau
Guillermo Valle Pérez
A. Louis
393
6
0
14 Feb 2021
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