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Kernel regression in high dimensions: Refined analysis beyond double
  descent

Kernel regression in high dimensions: Refined analysis beyond double descent

6 October 2020
Fanghui Liu
Zhenyu Liao
Johan A. K. Suykens
ArXivPDFHTML

Papers citing "Kernel regression in high dimensions: Refined analysis beyond double descent"

13 / 13 papers shown
Title
Characterizing Overfitting in Kernel Ridgeless Regression Through the
  Eigenspectrum
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
Tin Sum Cheng
Aurelien Lucchi
Anastasis Kratsios
David Belius
40
8
0
02 Feb 2024
Strong inductive biases provably prevent harmless interpolation
Strong inductive biases provably prevent harmless interpolation
Michael Aerni
Marco Milanta
Konstantin Donhauser
Fanny Yang
35
9
0
18 Jan 2023
On the Impossible Safety of Large AI Models
On the Impossible Safety of Large AI Models
El-Mahdi El-Mhamdi
Sadegh Farhadkhani
R. Guerraoui
Nirupam Gupta
L. Hoang
Rafael Pinot
Sébastien Rouault
John Stephan
30
31
0
30 Sep 2022
Benefit of Interpolation in Nearest Neighbor Algorithms
Benefit of Interpolation in Nearest Neighbor Algorithms
Yue Xing
Qifan Song
Guang Cheng
11
28
0
23 Feb 2022
Deformed semicircle law and concentration of nonlinear random matrices
  for ultra-wide neural networks
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
Zhichao Wang
Yizhe Zhu
35
18
0
20 Sep 2021
Towards an Understanding of Benign Overfitting in Neural Networks
Towards an Understanding of Benign Overfitting in Neural Networks
Zhu Li
Zhi-Hua Zhou
Arthur Gretton
MLT
33
35
0
06 Jun 2021
Adversarially Robust Kernel Smoothing
Adversarially Robust Kernel Smoothing
Jia-Jie Zhu
Christina Kouridi
Yassine Nemmour
Bernhard Schölkopf
28
7
0
16 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
35
135
0
16 Feb 2021
Multiple Descent: Design Your Own Generalization Curve
Multiple Descent: Design Your Own Generalization Curve
Lin Chen
Yifei Min
M. Belkin
Amin Karbasi
DRL
28
61
0
03 Aug 2020
Random Features for Kernel Approximation: A Survey on Algorithms,
  Theory, and Beyond
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu
Xiaolin Huang
Yudong Chen
Johan A. K. Suykens
BDL
44
172
0
23 Apr 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural
  Networks
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon
Abdulkadir Canatar
Cengiz Pehlevan
146
201
0
07 Feb 2020
Sharp analysis of low-rank kernel matrix approximations
Sharp analysis of low-rank kernel matrix approximations
Francis R. Bach
86
277
0
09 Aug 2012
Elastic-Net Regularization in Learning Theory
Elastic-Net Regularization in Learning Theory
C. D. Mol
E. De Vito
Lorenzo Rosasco
OOD
CML
110
306
0
22 Jul 2008
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