Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime

13 May 2022

Papers citing "Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime"

21 / 21 papers shown

Title
An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings Yue M. Lu H. Yau 42 25 0 12 May 2022
Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression Theodor Misiakiewicz 41 40 0 21 Apr 2022
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 71 111 0 26 Jan 2021
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization Ben Adlam Jeffrey Pennington 44 125 0 15 Aug 2020
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan 82 187 0 23 Jun 2020
Asymptotics of Ridge (less) Regression under General Source Condition Dominic Richards Jaouad Mourtada Lorenzo Rosasco 50 73 0 11 Jun 2020
$On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression$ On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression Denny Wu Ji Xu 63 122 0 10 Jun 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 139 152 0 02 Mar 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 205 206 0 07 Feb 2020
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 119 935 0 04 Dec 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 80 634 0 14 Aug 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 45 243 0 27 Apr 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 153 743 0 19 Mar 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon Alexander Rakhlin Xiyu Zhai 98 79 0 28 Dec 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 168 1,134 0 09 Nov 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 58 353 0 01 Aug 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 224 3,191 0 20 Jun 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 55 418 0 05 Feb 2018
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely Roy Frostig Y. Singer 136 343 0 18 Feb 2016
Ridge regression and asymptotic minimax estimation over spheres of growing dimension Lee H. Dicker 69 75 0 15 Jan 2016
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification Yan Sun Stefan Wager 85 289 0 10 Jul 2015