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Double Double Descent: On Generalization Errors in Transfer Learning
  between Linear Regression Tasks
v1v2v3v4v5v6v7v8 (latest)

Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks

SIAM Journal on Mathematics of Data Science (SIMODS), 2020
12 June 2020
Yehuda Dar
Richard G. Baraniuk
ArXiv (abs)PDFHTML

Papers citing "Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks"

13 / 13 papers shown
Source-Optimal Training is Transfer-Suboptimal
Source-Optimal Training is Transfer-Suboptimal
C. Evans Hedges
214
0
0
11 Nov 2025
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware
  Trajectory Prediction
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory PredictionInternational Conference on Pattern Recognition (ICPR), 2024
Qingze
Liu
Danrui Li
Samuel S. Sohn
Sejong Yoon
Mubbasir Kapadia
Vladimir Pavlovic
191
2
0
14 Oct 2024
Understanding the Role of Optimization in Double Descent
Understanding the Role of Optimization in Double Descent
Chris Yuhao Liu
Jeffrey Flanigan
310
0
0
06 Dec 2023
Generalization Performance of Transfer Learning: Overparameterized and
  Underparameterized Regimes
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
Peizhong Ju
Sen Lin
M. Squillante
Yitao Liang
Ness B. Shroff
253
4
0
08 Jun 2023
Optimal transfer protocol by incremental layer defrosting
Optimal transfer protocol by incremental layer defrosting
Federica Gerace
Diego Doimo
Stefano Sarao Mannelli
Luca Saglietti
Alessandro Laio
CLL
203
3
0
02 Mar 2023
Frozen Overparameterization: A Double Descent Perspective on Transfer
  Learning of Deep Neural Networks
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks
Yehuda Dar
Lorenzo Luzi
Richard G. Baraniuk
AI4CE
246
2
0
20 Nov 2022
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of
  Overparameterized Machine Learning
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar
Vidya Muthukumar
Richard G. Baraniuk
316
79
0
06 Sep 2021
Probing transfer learning with a model of synthetic correlated datasets
Probing transfer learning with a model of synthetic correlated datasets
Federica Gerace
Luca Saglietti
Stefano Sarao Mannelli
Andrew M. Saxe
Lenka Zdeborová
OOD
297
41
0
09 Jun 2021
Double Descent and Other Interpolation Phenomena in GANs
Double Descent and Other Interpolation Phenomena in GANs
Lorenzo Luzi
Yehuda Dar
Richard Baraniuk
350
5
0
07 Jun 2021
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies
  for Linear Regression
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear RegressionSIAM Journal on Mathematics of Data Science (SIMODS), 2021
Yehuda Dar
Daniel LeJeune
Richard G. Baraniuk
MLT
270
8
0
09 Mar 2021
Transfer Learning for Linear Regression: a Statistical Test of Gain
Transfer Learning for Linear Regression: a Statistical Test of Gain
David Obst
B. Ghattas
Jairo Cugliari
G. Oppenheim
Sandra Claudel
Y. Goude
175
14
0
18 Feb 2021
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons
Phase Transitions in Transfer Learning for High-Dimensional PerceptronsEntropy (Entropy), 2021
Oussama Dhifallah
Yue M. Lu
195
25
0
06 Jan 2021
Provable More Data Hurt in High Dimensional Least Squares Estimator
Provable More Data Hurt in High Dimensional Least Squares Estimator
Zeng Li
Chuanlong Xie
Qinwen Wang
182
6
0
14 Aug 2020
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