Does data interpolation contradict statistical optimality?

25 June 2018

Papers citing "Does data interpolation contradict statistical optimality?"

50 / 52 papers shown

Title
Supervised Kernel Thinning Albert Gong Kyuseong Choi Raaz Dwivedi 31 0 0 17 Oct 2024
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent Michael Kohler A. Krzyżak Benjamin Walter 36 1 0 13 May 2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation Aaron Mishkin Mert Pilanci Mark Schmidt 64 1 0 03 Apr 2024
Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization Selina Drews Michael Kohler 36 3 0 24 Nov 2023
A decorrelation method for general regression adjustment in randomized experiments Fangzhou Su Wenlong Mou Peng Ding Martin J. Wainwright 24 1 0 16 Nov 2023
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data Zhiwei Xu Yutong Wang Spencer Frei Gal Vardi Wei Hu MLT 28 24 0 04 Oct 2023
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks Vignesh Kothapalli Tom Tirer Joan Bruna 38 11 0 04 Jul 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 35 14 0 23 May 2023
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method Siwei Wang S. Palmer 32 2 0 19 May 2023
A Simple Algorithm For Scaling Up Kernel Methods Tengyu Xu Bryan Kelly Semyon Malamud 21 0 0 26 Jan 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 35 9 0 18 Jan 2023
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks Ilja Kuzborskij Csaba Szepesvári 21 4 0 28 Dec 2022
Private optimization in the interpolation regime: faster rates and hardness results Hilal Asi Karan N. Chadha Gary Cheng John C. Duchi 47 5 0 31 Oct 2022
Perturbation Analysis of Neural Collapse Tom Tirer Haoxiang Huang Jonathan Niles-Weed AAML 46 24 0 29 Oct 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do Niladri S. Chatterji Philip M. Long 23 8 0 19 Sep 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting Neil Rohit Mallinar James B. Simon Amirhesam Abedsoltan Parthe Pandit M. Belkin Preetum Nakkiran 26 37 0 14 Jul 2022
Benefits of Additive Noise in Composing Classes with Bounded Capacity A. F. Pour H. Ashtiani 33 3 0 14 Jun 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 14 28 0 23 Feb 2022
HARFE: Hard-Ridge Random Feature Expansion Esha Saha Hayden Schaeffer Giang Tran 38 14 0 06 Feb 2022
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model A. Bodin N. Macris 37 13 0 22 Oct 2021
Conditioning of Random Feature Matrices: Double Descent and Generalization Error Zhijun Chen Hayden Schaeffer 37 12 0 21 Oct 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 34 71 0 06 Sep 2021
Knowledge Distillation as Semiparametric Inference Tri Dao G. Kamath Vasilis Syrgkanis Lester W. Mackey 40 31 0 20 Apr 2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini Orestis Paraskevas Samet Oymak Christos Thrampoulidis 29 93 0 02 Mar 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 18 0 18 Feb 2021
SGD Generalizes Better Than GD (And Regularization Doesn't Help) I Zaghloul Amir Tomer Koren Roi Livni 21 46 0 01 Feb 2021
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Xiangyu Chang Yingcong Li Samet Oymak Christos Thrampoulidis 35 50 0 16 Dec 2020
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition Ben Adlam Jeffrey Pennington UD 39 93 0 04 Nov 2020
Prevalence of Neural Collapse during the terminal phase of deep learning training Vardan Papyan Xuemei Han D. Donoho 35 549 0 18 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 46 440 0 09 Aug 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 28 61 0 03 Aug 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip Torr NoLa AAML 23 57 0 08 Jul 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 28 3 0 19 Jun 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 36 32 0 18 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi Ernesto De Vito Lorenzo Rosasco 44 8 0 17 Jun 2020
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information Esther Rolf Michael I. Jordan Benjamin Recht 11 6 0 12 Mar 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 146 201 0 07 Feb 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 35 68 0 05 Feb 2020
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 27 77 0 10 Dec 2019
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 36 145 0 13 Nov 2019
The Role of Neural Network Activation Functions Rahul Parhi Robert D. Nowak 26 12 0 05 Oct 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 60 626 0 14 Aug 2019
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 8 762 0 26 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 49 481 0 12 Jun 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 18 241 0 27 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 60 1,610 0 28 Dec 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 26 117 0 17 Oct 2018
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 21 123 0 12 Oct 2018