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Consistency of Interpolation with Laplace Kernels is a High-Dimensional
  Phenomenon

Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon

28 December 2018
Alexander Rakhlin
Xiyu Zhai
ArXiv (abs)PDFHTML

Papers citing "Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon"

28 / 28 papers shown
Title
Feature maps for the Laplacian kernel and its generalizations
Feature maps for the Laplacian kernel and its generalizations
Sudhendu Ahir
Parthe Pandit
101
0
0
24 Feb 2025
On the Pinsker bound of inner product kernel regression in large dimensions
On the Pinsker bound of inner product kernel regression in large dimensions
Weihao Lu
Jialin Ding
Haobo Zhang
Qian Lin
93
1
0
02 Sep 2024
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel
  Learning
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
Fan He
Mingzhe He
Lei Shi
Xiaolin Huang
Johan A. K. Suykens
68
1
0
03 Jun 2024
Six Lectures on Linearized Neural Networks
Six Lectures on Linearized Neural Networks
Theodor Misiakiewicz
Andrea Montanari
137
13
0
25 Aug 2023
How many samples are needed to leverage smoothness?
How many samples are needed to leverage smoothness?
Vivien A. Cabannes
Stefano Vigogna
62
2
0
25 May 2023
From Tempered to Benign Overfitting in ReLU Neural Networks
From Tempered to Benign Overfitting in ReLU Neural Networks
Guy Kornowski
Gilad Yehudai
Ohad Shamir
91
13
0
24 May 2023
Mind the spikes: Benign overfitting of kernels and neural networks in
  fixed dimension
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
Moritz Haas
David Holzmüller
U. V. Luxburg
Ingo Steinwart
MLT
102
14
0
23 May 2023
Kernel interpolation generalizes poorly
Kernel interpolation generalizes poorly
Yicheng Li
Haobo Zhang
Qian Lin
73
11
0
28 Mar 2023
Strong inductive biases provably prevent harmless interpolation
Strong inductive biases provably prevent harmless interpolation
Michael Aerni
Marco Milanta
Konstantin Donhauser
Fanny Yang
87
9
0
18 Jan 2023
Learning Lipschitz Functions by GD-trained Shallow Overparameterized
  ReLU Neural Networks
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
Ilja Kuzborskij
Csaba Szepesvári
75
4
0
28 Dec 2022
Rates of Convergence for Regression with the Graph Poly-Laplacian
Rates of Convergence for Regression with the Graph Poly-Laplacian
Nicolas García Trillos
Ryan W. Murray
Matthew Thorpe
93
5
0
06 Sep 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
Spectrum of inner-product kernel matrices in the polynomial regime and
  multiple descent phenomenon in kernel ridge regression
Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression
Theodor Misiakiewicz
67
40
0
21 Apr 2022
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total
  Variation
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
Shayan Aziznejad
Joaquim Campos
M. Unser
93
10
0
12 Dec 2021
Harmless interpolation in regression and classification with structured
  features
Harmless interpolation in regression and classification with structured features
Andrew D. McRae
Santhosh Karnik
Mark A. Davenport
Vidya Muthukumar
186
11
0
09 Nov 2021
Classification and Adversarial examples in an Overparameterized Linear
  Model: A Signal Processing Perspective
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective
Adhyyan Narang
Vidya Muthukumar
A. Sahai
SILMAAML
69
1
0
27 Sep 2021
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping
Ilja Kuzborskij
Csaba Szepesvári
100
7
0
12 Jul 2021
Towards an Understanding of Benign Overfitting in Neural Networks
Towards an Understanding of Benign Overfitting in Neural Networks
Zhu Li
Zhi Zhou
Arthur Gretton
MLT
105
35
0
06 Jun 2021
Fitting Elephants
Fitting Elephants
P. Mitra
26
0
0
31 Mar 2021
What Neural Networks Memorize and Why: Discovering the Long Tail via
  Influence Estimation
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman
Chiyuan Zhang
TDI
248
472
0
09 Aug 2020
Multiple Descent: Design Your Own Generalization Curve
Multiple Descent: Design Your Own Generalization Curve
Lin Chen
Yifei Min
M. Belkin
Amin Karbasi
DRL
162
61
0
03 Aug 2020
Interpolation and Learning with Scale Dependent Kernels
Nicolò Pagliana
Alessandro Rudi
Ernesto De Vito
Lorenzo Rosasco
91
8
0
17 Jun 2020
Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery
  Perspective
Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery Perspective
S. Foucart
Chunyang Liao
Shahin Shahrampour
Yinsong Wang
41
0
0
05 Jun 2020
The Generalization Error of the Minimum-norm Solutions for
  Over-parameterized Neural Networks
The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks
E. Weinan
Chao Ma
Lei Wu
49
14
0
15 Dec 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and
  Generalization
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
T. Poggio
Andrzej Banburski
Q. Liao
ODL
126
165
0
25 Aug 2019
The generalization error of random features regression: Precise
  asymptotics and double descent curve
The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei
Andrea Montanari
162
640
0
14 Aug 2019
Does Learning Require Memorization? A Short Tale about a Long Tail
Does Learning Require Memorization? A Short Tale about a Long Tail
Vitaly Feldman
TDI
194
504
0
12 Jun 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
302
747
0
19 Mar 2019
1