A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors

23 July 2022

Papers citing "A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors"

27 / 27 papers shown

Title
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization Spencer Frei Gal Vardi Peter L. Bartlett Nathan Srebro 41 23 0 02 Mar 2023
Benign overfitting and adaptive nonparametric regression J. Chhor Suzanne Sigalla Alexandre B. Tsybakov 25 3 0 27 Jun 2022
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression Chen Cheng John C. Duchi Rohith Kuditipudi 21 10 0 20 Feb 2022
Benign Overfitting in Two-layer Convolutional Neural Networks Yuan Cao Zixiang Chen M. Belkin Quanquan Gu MLT 41 88 0 14 Feb 2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data Spencer Frei Niladri S. Chatterji Peter L. Bartlett MLT 51 72 0 11 Feb 2022
Foolish Crowds Support Benign Overfitting Niladri S. Chatterji Philip M. Long 103 20 0 06 Oct 2021
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting Frederic Koehler Lijia Zhou Danica J. Sutherland Nathan Srebro 61 56 0 17 Jun 2021
Towards an Understanding of Benign Overfitting in Neural Networks Zhu Li Zhi Zhou Arthur Gretton MLT 62 35 0 06 Jun 2021
A Universal Law of Robustness via Isoperimetry Sébastien Bubeck Mark Sellke 27 215 0 26 May 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou Jingfeng Wu Vladimir Braverman Quanquan Gu Sham Kakade 22 63 0 23 Mar 2021
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 53 111 0 26 Jan 2021
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 272 93 0 11 Dec 2020
On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller 20 12 0 05 Oct 2020
Benign overfitting in ridge regression Alexander Tsigler Peter L. Bartlett 40 164 0 29 Sep 2020
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization Ben Adlam Jeffrey Pennington 25 123 0 15 Aug 2020
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 68 631 0 14 Aug 2019
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 44 769 0 26 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 113 489 0 12 Jun 2019
Harmless interpolation of noisy data in regression Vidya Muthukumar Kailas Vodrahalli Vignesh Subramanian A. Sahai 45 204 0 21 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 113 737 0 19 Mar 2019
Two models of double descent for weak features M. Belkin Daniel J. Hsu Ji Xu 77 375 0 18 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 166 1,628 0 28 Dec 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 34 353 0 01 Aug 2018
Does data interpolation contradict statistical optimality? M. Belkin Alexander Rakhlin Alexandre B. Tsybakov 57 218 0 25 Jun 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 122 256 0 13 Jun 2018
Ridge regression and asymptotic minimax estimation over spheres of growing dimension Lee H. Dicker 56 75 0 15 Jan 2016
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification Yan Sun Stefan Wager 41 288 0 10 Jul 2015