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Towards Data-Algorithm Dependent Generalization: a Case Study on
  Overparameterized Linear Regression
v1v2v3v4 (latest)

Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression

12 February 2022
Jing Xu
Jiaye Teng
Yang Yuan
Andrew Chi-Chih Yao
ArXiv (abs)PDFHTML

Papers citing "Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression"

21 / 21 papers shown
Title
On Emergence of Clean-Priority Learning in Early Stopped Neural Networks
On Emergence of Clean-Priority Learning in Early Stopped Neural Networks
Chaoyue Liu
Amirhesam Abedsoltan
M. Belkin
NoLa
61
5
0
05 Jun 2023
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High
  Dimensions
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Mojtaba Sahraee-Ardakan
M. Emami
Parthe Pandit
S. Rangan
A. Fletcher
83
9
0
20 Jan 2022
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
Jiangyuan Li
Thanh V. Nguyen
Chinmay Hegde
R. K. Wong
70
30
0
12 Aug 2021
Understanding and Improving Early Stopping for Learning with Noisy
  Labels
Understanding and Improving Early Stopping for Learning with Noisy Labels
Ying-Long Bai
Erkun Yang
Bo Han
Yanhua Yang
Jiatong Li
Yinian Mao
Gang Niu
Tongliang Liu
NoLa
66
221
0
30 Jun 2021
Early-stopped neural networks are consistent
Early-stopped neural networks are consistent
Ziwei Ji
Justin D. Li
Matus Telgarsky
67
37
0
10 Jun 2021
Information Complexity and Generalization Bounds
Information Complexity and Generalization Bounds
P. Banerjee
Guido Montúfar
52
14
0
04 May 2021
Re-labeling ImageNet: from Single to Multi-Labels, from Global to
  Localized Labels
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
Sangdoo Yun
Seong Joon Oh
Byeongho Heo
Dongyoon Han
Junsuk Choe
Sanghyuk Chun
495
147
0
13 Jan 2021
The Pitfalls of Simplicity Bias in Neural Networks
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah
Kaustav Tamuly
Aditi Raghunathan
Prateek Jain
Praneeth Netrapalli
AAML
69
363
0
13 Jun 2020
On Uniform Convergence and Low-Norm Interpolation Learning
On Uniform Convergence and Low-Norm Interpolation Learning
Lijia Zhou
Danica J. Sutherland
Nathan Srebro
62
29
0
10 Jun 2020
The Statistical Complexity of Early-Stopped Mirror Descent
The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaskevicius
Varun Kanade
Patrick Rebeschini
58
23
0
01 Feb 2020
In Defense of Uniform Convergence: Generalization via derandomization
  with an application to interpolating predictors
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors
Jeffrey Negrea
Gintare Karolina Dziugaite
Daniel M. Roy
AI4CE
64
65
0
09 Dec 2019
Fantastic Generalization Measures and Where to Find Them
Fantastic Generalization Measures and Where to Find Them
Yiding Jiang
Behnam Neyshabur
H. Mobahi
Dilip Krishnan
Samy Bengio
AI4CE
145
611
0
04 Dec 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu
Jian Li
98
336
0
13 Jun 2019
A Continuous-Time View of Early Stopping for Least Squares
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali
J. Zico Kolter
Robert Tibshirani
67
97
0
23 Oct 2018
Stronger generalization bounds for deep nets via a compression approach
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora
Rong Ge
Behnam Neyshabur
Yi Zhang
MLTAI4CE
89
643
0
14 Feb 2018
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for
  Neural Networks
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur
Srinadh Bhojanapalli
Nathan Srebro
86
610
0
29 Jul 2017
Spectrally-normalized margin bounds for neural networks
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett
Dylan J. Foster
Matus Telgarsky
ODL
212
1,225
0
26 Jun 2017
Information-theoretic analysis of generalization capability of learning
  algorithms
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu
Maxim Raginsky
183
448
0
22 May 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural
  Networks with Many More Parameters than Training Data
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Gintare Karolina Dziugaite
Daniel M. Roy
117
819
0
31 Mar 2017
A PAC-Bayesian Tutorial with A Dropout Bound
A PAC-Bayesian Tutorial with A Dropout Bound
David A. McAllester
83
140
0
08 Jul 2013
Online Learning as Stochastic Approximation of Regularization Paths
Online Learning as Stochastic Approximation of Regularization Paths
P. Tarres
Yuan Yao
81
94
0
29 Mar 2011
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