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The Implicit Regularization of Stochastic Gradient Flow for Least
  Squares

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

17 March 2020
Alnur Ali
Edgar Dobriban
R. Tibshirani
ArXivPDFHTML

Papers citing "The Implicit Regularization of Stochastic Gradient Flow for Least Squares"

18 / 18 papers shown
Title
Comparing regularisation paths of (conjugate) gradient estimators in ridge regression
Laura Hucker
Markus Reiß
Thomas Stark
33
1
0
07 Mar 2025
Generalized equivalences between subsampling and ridge regularization
Generalized equivalences between subsampling and ridge regularization
Pratik V. Patil
Jin-Hong Du
24
5
0
29 May 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Bhavya Agrawalla
Krishnakumar Balasubramanian
Promit Ghosal
23
2
0
20 Feb 2023
Toward Equation of Motion for Deep Neural Networks: Continuous-time
  Gradient Descent and Discretization Error Analysis
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
Taiki Miyagawa
35
9
0
28 Oct 2022
Label noise (stochastic) gradient descent implicitly solves the Lasso
  for quadratic parametrisation
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation
Loucas Pillaud-Vivien
J. Reygner
Nicolas Flammarion
NoLa
33
31
0
20 Jun 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic
  Equations via Gradient Descent
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
Yiping Lu
Jose H. Blanchet
Lexing Ying
32
7
0
15 May 2022
The Directional Bias Helps Stochastic Gradient Descent to Generalize in
  Kernel Regression Models
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models
Yiling Luo
X. Huo
Y. Mei
11
0
0
29 Apr 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different
  Learning Regimes
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes
Elvis Dohmatob
A. Bietti
AAML
21
13
0
22 Mar 2022
A Note on Machine Learning Approach for Computational Imaging
A Note on Machine Learning Approach for Computational Imaging
Bin Dong
17
0
0
24 Feb 2022
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Kexin Jin
J. Latz
Chenguang Liu
Carola-Bibiane Schönlieb
18
9
0
07 Dec 2021
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki
Amartya Mitra
Yoshua Bengio
Guillaume Lajoie
61
25
0
06 Dec 2021
AgFlow: Fast Model Selection of Penalized PCA via Implicit
  Regularization Effects of Gradient Flow
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow
Haiyan Jiang
Haoyi Xiong
Dongrui Wu
Ji Liu
Dejing Dou
13
1
0
07 Oct 2021
Interpolation can hurt robust generalization even when there is no noise
Interpolation can hurt robust generalization even when there is no noise
Konstantin Donhauser
Alexandru cTifrea
Michael Aerni
Reinhard Heckel
Fanny Yang
31
14
0
05 Aug 2021
The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations,
  and Anomalous Diffusion
The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion
D. Kunin
Javier Sagastuy-Breña
Lauren Gillespie
Eshed Margalit
Hidenori Tanaka
Surya Ganguli
Daniel L. K. Yamins
31
15
0
19 Jul 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg
Sivaraman Balakrishnan
J. Zico Kolter
Zachary Chase Lipton
28
29
0
01 May 2021
Group-Sparse Matrix Factorization for Transfer Learning of Word
  Embeddings
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Kan Xu
Xuanyi Zhao
Hamsa Bastani
Osbert Bastani
25
6
0
18 Apr 2021
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Noam Razin
Nadav Cohen
16
155
0
13 May 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
133
1,198
0
16 Aug 2016
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