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Benign Overfitting without Linearity: Neural Network Classifiers Trained
  by Gradient Descent for Noisy Linear Data

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data

11 February 2022
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
    MLT
ArXivPDFHTML

Papers citing "Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data"

18 / 18 papers shown
Title
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying
  Bandwidth or Dimensionality
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
Marko Medvedev
Gal Vardi
Nathan Srebro
59
3
0
05 Sep 2024
Bayesian Inference for Consistent Predictions in Overparameterized
  Nonlinear Regression
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
Tomoya Wakayama
BDL
57
0
0
06 Apr 2024
Implicit Bias and Fast Convergence Rates for Self-attention
Implicit Bias and Fast Convergence Rates for Self-attention
Bhavya Vasudeva
Puneesh Deora
Christos Thrampoulidis
26
13
0
08 Feb 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax
  Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Hyunouk Ko
Xiaoming Huo
28
1
0
08 Jan 2024
Analysis of the expected $L_2$ error of an over-parametrized deep neural
  network estimate learned by gradient descent without regularization
Analysis of the expected L2L_2L2​ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
Selina Drews
Michael Kohler
25
2
0
24 Nov 2023
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data
Zhiwei Xu
Yutong Wang
Spencer Frei
Gal Vardi
Wei Hu
MLT
28
23
0
04 Oct 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
20
12
0
24 May 2023
On the Eigenvalue Decay Rates of a Class of Neural-Network Related
  Kernel Functions Defined on General Domains
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
Yicheng Li
Zixiong Yu
Y. Cotronis
Qian Lin
55
13
0
04 May 2023
General Loss Functions Lead to (Approximate) Interpolation in High
  Dimensions
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
Kuo-Wei Lai
Vidya Muthukumar
18
5
0
13 Mar 2023
The Curious Case of Benign Memorization
The Curious Case of Benign Memorization
Sotiris Anagnostidis
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
AAML
41
8
0
25 Oct 2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do
Deep Linear Networks can Benignly Overfit when Shallow Ones Do
Niladri S. Chatterji
Philip M. Long
17
8
0
19 Sep 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Rohit Mallinar
James B. Simon
Amirhesam Abedsoltan
Parthe Pandit
M. Belkin
Preetum Nakkiran
24
37
0
14 Jul 2022
Random Feature Amplification: Feature Learning and Generalization in
  Neural Networks
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
30
29
0
15 Feb 2022
Benign Overfitting in Adversarially Robust Linear Classification
Benign Overfitting in Adversarially Robust Linear Classification
Jinghui Chen
Yuan Cao
Quanquan Gu
AAML
SILM
28
10
0
31 Dec 2021
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
79
20
0
10 Nov 2021
Foolish Crowds Support Benign Overfitting
Foolish Crowds Support Benign Overfitting
Niladri S. Chatterji
Philip M. Long
83
20
0
06 Oct 2021
Understanding the Generalization of Adam in Learning Neural Networks
  with Proper Regularization
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Difan Zou
Yuan Cao
Yuanzhi Li
Quanquan Gu
MLT
AI4CE
44
37
0
25 Aug 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in
  the Presence of Adversarial Label Noise
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei
Yuan Cao
Quanquan Gu
FedML
MLT
60
18
0
04 Jan 2021
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