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  3. 2011.03321
  4. Cited By
Understanding Double Descent Requires a Fine-Grained Bias-Variance
  Decomposition

Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition

4 November 2020
Ben Adlam
Jeffrey Pennington
    UD
ArXiv (abs)PDFHTML

Papers citing "Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition"

50 / 68 papers shown
Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
Anvit Garg
Sohom Bhattacharya
Pragya Sur
193
3
0
26 Sep 2025
Convergence and Generalization of Anti-Regularization for Parametric Models
Convergence and Generalization of Anti-Regularization for Parametric Models
Dongseok Kim
Wonjun Jeong
Gisung Oh
284
0
0
24 Aug 2025
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
Arjun Subramonian
Elvis Dohmatob
256
0
0
14 Apr 2025
Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks
Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks
Rylan Schaeffer
Punit Singh Koura
Binh Tang
R. Subramanian
Aaditya K. Singh
...
Vedanuj Goswami
Sergey Edunov
Dieuwke Hupkes
Sanmi Koyejo
Sharan Narang
ALM
453
2
0
24 Feb 2025
Analysis of Overparameterization in Continual Learning under a Linear Model
Analysis of Overparameterization in Continual Learning under a Linear Model
Daniel Goldfarb
Paul Hand
CLL
305
2
0
11 Feb 2025
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer
Blake Bordelon
Cengiz Pehlevan
AI4CE
793
12
0
04 Feb 2025
Theoretical Limitations of Ensembles in the Age of Overparameterization
Theoretical Limitations of Ensembles in the Age of Overparameterization
Niclas Dern
John P. Cunningham
Geoff Pleiss
BDLUQCV
422
3
0
21 Oct 2024
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A
  Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random
  Designs
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs
Alicia Curth
309
6
0
27 Sep 2024
Unified Neural Network Scaling Laws and Scale-time Equivalence
Unified Neural Network Scaling Laws and Scale-time Equivalence
Akhilan Boopathy
Ila Fiete
548
3
0
09 Sep 2024
Risk and cross validation in ridge regression with correlated samples
Risk and cross validation in ridge regression with correlated samples
Alexander B. Atanasov
Jacob A. Zavatone-Veth
Cengiz Pehlevan
583
8
0
08 Aug 2024
Exploring Loss Landscapes through the Lens of Spin Glass Theory
Exploring Loss Landscapes through the Lens of Spin Glass Theory
Hao Liao
Wei Zhang
Zhanyi Huang
Zexiao Long
Mingyang Zhou
Xiaoqun Wu
Rui Mao
Chi Ho Yeung
307
3
0
30 Jul 2024
Towards understanding epoch-wise double descent in two-layer linear
  neural networks
Towards understanding epoch-wise double descent in two-layer linear neural networks
Amanda Olmin
Fredrik Lindsten
MLT
333
4
0
13 Jul 2024
Towards an Improved Understanding and Utilization of Maximum Manifold
  Capacity Representations
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
Rylan Schaeffer
Victor Lecomte
Dhruv Pai
Andres Carranza
Berivan Isik
...
Yann LeCun
SueYeon Chung
Andrey Gromov
Ravid Shwartz-Ziv
Sanmi Koyejo
315
10
0
13 Jun 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Yufan Li
Subhabrata Sen
Ben Adlam
MLT
505
2
0
18 Apr 2024
Failures and Successes of Cross-Validation for Early-Stopped Gradient
  Descent
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
Pratik Patil
Yuchen Wu
Robert Tibshirani
273
6
0
26 Feb 2024
A Dynamical Model of Neural Scaling Laws
A Dynamical Model of Neural Scaling Laws
Blake Bordelon
Alexander B. Atanasov
Cengiz Pehlevan
847
82
0
02 Feb 2024
AEDFL: Efficient Asynchronous Decentralized Federated Learning with
  Heterogeneous Devices
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
Ji Liu
Tianshi Che
Yang Zhou
Ruoming Jin
H. Dai
Dejing Dou
P. Valduriez
289
22
0
18 Dec 2023
Foundation Model's Embedded Representations May Detect Distribution
  Shift
Foundation Model's Embedded Representations May Detect Distribution Shift
Max Vargas
Adam Tsou
A. Engel
Tony Chiang
254
1
0
20 Oct 2023
Neural Tangent Kernels Motivate Graph Neural Networks with
  Cross-Covariance Graphs
Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs
Shervin Khalafi
Saurabh Sihag
Alejandro Ribeiro
270
0
0
16 Oct 2023
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep
  Models
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models
Lin Chen
Michal Lukasik
Wittawat Jitkrittum
Chong You
Sanjiv Kumar
394
1
0
13 Oct 2023
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural NetworksInternational Conference on Machine Learning (ICML), 2023
Behrad Moniri
Donghwan Lee
Hamed Hassani
Guang Cheng
MLT
585
36
0
11 Oct 2023
Corrected generalized cross-validation for finite ensembles of penalized
  estimators
Corrected generalized cross-validation for finite ensembles of penalized estimators
Pierre C. Bellec
Jin-Hong Du
Takuya Koriyama
Pratik Patil
Kai Tan
443
6
0
02 Oct 2023
Adversarial Training with Generated Data in High-Dimensional Regression:
  An Asymptotic Study
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
Yue Xing
228
1
0
21 Jun 2023
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Gibbs-Based Information Criteria and the Over-Parameterized RegimeInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Haobo Chen
Yuheng Bu
Greg Wornell
392
1
0
08 Jun 2023
Federated Learning under Covariate Shifts with Generalization Guarantees
Federated Learning under Covariate Shifts with Generalization Guarantees
Ali Ramezani-Kebrya
Fanghui Liu
Thomas Pethick
Grigorios G. Chrysos
Volkan Cevher
FedMLOOD
384
12
0
08 Jun 2023
Generalized equivalences between subsampling and ridge regularization
Generalized equivalences between subsampling and ridge regularizationNeural Information Processing Systems (NeurIPS), 2023
Pratik V. Patil
Jin-Hong Du
371
6
0
29 May 2023
Feature-Learning Networks Are Consistent Across Widths At Realistic
  Scales
Feature-Learning Networks Are Consistent Across Widths At Realistic ScalesNeural Information Processing Systems (NeurIPS), 2023
Nikhil Vyas
Alexander B. Atanasov
Blake Bordelon
Depen Morwani
Sabarish Sainathan
Cengiz Pehlevan
481
43
0
28 May 2023
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Subsample Ridge Ensembles: Equivalences and Generalized Cross-ValidationInternational Conference on Machine Learning (ICML), 2023
Jin-Hong Du
Pratik V. Patil
Arun K. Kuchibhotla
265
12
0
25 Apr 2023
Censoring chemical data to mitigate dual use risk
Censoring chemical data to mitigate dual use risk
Quintina Campbell
J. Herington
Andrew D. White
AAML
358
9
0
20 Apr 2023
Double Descent Demystified: Identifying, Interpreting & Ablating the
  Sources of a Deep Learning Puzzle
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Rylan Schaeffer
Mikail Khona
Zachary Robertson
Akhilan Boopathy
Kateryna Pistunova
J. Rocks
Ila Rani Fiete
Oluwasanmi Koyejo
349
48
0
24 Mar 2023
Learning curves for deep structured Gaussian feature models
Learning curves for deep structured Gaussian feature modelsNeural Information Processing Systems (NeurIPS), 2023
Jacob A. Zavatone-Veth
Cengiz Pehlevan
MLT
368
14
0
01 Mar 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective
  Self-play
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
J. Liu
Krishnamurthy Dvijotham
Jihyeon Janel Lee
Quan Yuan
Martin Strobel
Balaji Lakshminarayanan
Deepak Ramachandran
259
5
0
11 Feb 2023
Pathologies of Predictive Diversity in Deep Ensembles
Pathologies of Predictive Diversity in Deep Ensembles
Taiga Abe
E. Kelly Buchanan
Geoff Pleiss
John P. Cunningham
UQCV
427
21
0
01 Feb 2023
Deterministic equivalent and error universality of deep random features
  learning
Deterministic equivalent and error universality of deep random features learningInternational Conference on Machine Learning (ICML), 2023
Dominik Schröder
Hugo Cui
Daniil Dmitriev
Bruno Loureiro
MLT
335
36
0
01 Feb 2023
Demystifying Disagreement-on-the-Line in High Dimensions
Demystifying Disagreement-on-the-Line in High DimensionsInternational Conference on Machine Learning (ICML), 2023
Dong-Hwan Lee
Behrad Moniri
Xinmeng Huang
Guang Cheng
Hamed Hassani
377
12
0
31 Jan 2023
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich
  Regimes
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
Alexander B. Atanasov
Blake Bordelon
Sabarish Sainathan
Cengiz Pehlevan
383
30
0
23 Dec 2022
Gradient flow in the gaussian covariate model: exact solution of
  learning curves and multiple descent structures
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures
Antione Bodin
N. Macris
291
5
0
13 Dec 2022
Uncertainty Estimates of Predictions via a General Bias-Variance
  Decomposition
Uncertainty Estimates of Predictions via a General Bias-Variance DecompositionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Sebastian G. Gruber
Florian Buettner
PERUQCVUD
630
21
0
21 Oct 2022
Multiple Descent in the Multiple Random Feature Model
Multiple Descent in the Multiple Random Feature ModelJournal of machine learning research (JMLR), 2022
Xuran Meng
Jianfeng Yao
Yuan Cao
296
10
0
21 Aug 2022
Investigating the Impact of Model Width and Density on Generalization in
  Presence of Label Noise
Investigating the Impact of Model Width and Density on Generalization in Presence of Label NoiseConference on Uncertainty in Artificial Intelligence (UAI), 2022
Yihao Xue
Kyle Whitecross
Baharan Mirzasoleiman
NoLa
453
2
0
17 Aug 2022
Ensembling over Classifiers: a Bias-Variance Perspective
Ensembling over Classifiers: a Bias-Variance Perspective
Neha Gupta
Jamie Smith
Ben Adlam
Zelda E. Mariet
FedMLUQCVFaML
207
9
0
21 Jun 2022
Implicit Regularization or Implicit Conditioning? Exact Risk
  Trajectories of SGD in High Dimensions
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High DimensionsNeural Information Processing Systems (NeurIPS), 2022
Courtney Paquette
Elliot Paquette
Ben Adlam
Jeffrey Pennington
237
19
0
15 Jun 2022
Regularization-wise double descent: Why it occurs and how to eliminate
  it
Regularization-wise double descent: Why it occurs and how to eliminate itInternational Symposium on Information Theory (ISIT), 2022
Fatih Yilmaz
Reinhard Heckel
238
11
0
03 Jun 2022
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product
  Kernel Regression
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel RegressionJournal of Statistical Mechanics: Theory and Experiment (JSTAT), 2022
Lechao Xiao
Hong Hu
Theodor Misiakiewicz
Yue M. Lu
Jeffrey Pennington
394
23
0
30 May 2022
Bias-Variance Decompositions for Margin Losses
Bias-Variance Decompositions for Margin LossesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Danny Wood
Tingting Mu
Gavin Brown
UQCV
252
7
0
26 Apr 2022
Can Neural Nets Learn the Same Model Twice? Investigating
  Reproducibility and Double Descent from the Decision Boundary Perspective
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary PerspectiveComputer Vision and Pattern Recognition (CVPR), 2022
Gowthami Somepalli
Liam H. Fowl
Arpit Bansal
Ping Yeh-Chiang
Yehuda Dar
Richard Baraniuk
Micah Goldblum
Tom Goldstein
230
78
0
15 Mar 2022
Bias-variance decomposition of overparameterized regression with random
  linear features
Bias-variance decomposition of overparameterized regression with random linear featuresPhysical Review E (Phys. Rev. E), 2022
J. Rocks
Pankaj Mehta
193
12
0
10 Mar 2022
Generalization Through The Lens Of Leave-One-Out Error
Generalization Through The Lens Of Leave-One-Out ErrorInternational Conference on Learning Representations (ICLR), 2022
Gregor Bachmann
Thomas Hofmann
Aurelien Lucchi
368
8
0
07 Mar 2022
Contrasting random and learned features in deep Bayesian linear
  regression
Contrasting random and learned features in deep Bayesian linear regressionPhysical Review E (Phys. Rev. E), 2022
Jacob A. Zavatone-Veth
William L. Tong
Cengiz Pehlevan
BDLMLT
393
31
0
01 Mar 2022
Deep Ensembles Work, But Are They Necessary?
Deep Ensembles Work, But Are They Necessary?Neural Information Processing Systems (NeurIPS), 2022
Taiga Abe
E. Kelly Buchanan
Geoff Pleiss
R. Zemel
John P. Cunningham
OODUQCV
433
86
0
14 Feb 2022
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