Kernel Alignment Risk Estimator: Risk Prediction from Training Data

Kernel Alignment Risk Estimator: Risk Prediction from Training Data

17 June 2020

Berfin cSimcsek

Francesco Spadaro

Clément Hongler

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Papers citing "Kernel Alignment Risk Estimator: Risk Prediction from Training Data"

18 / 18 papers shown

Title
Training NTK to Generalize with KARE Johannes Schwab Bryan Kelly Semyon Malamud Teng Andrea Xu 119 0 0 16 May 2025
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking) Yoonsoo Nam Seok Hyeong Lee Clementine Domine Yea Chan Park Charles London Wonyl Choi Niclas Goring Seungjai Lee AI4CE 174 1 0 28 Feb 2025
Neural Kernels Without Tangents Vaishaal Shankar Alex Fang Wenshuo Guo Sara Fridovich-Keil Ludwig Schmidt Jonathan Ragan-Kelley Benjamin Recht 46 91 0 04 Mar 2020
Implicit Regularization of Random Feature Models Arthur Jacot Berfin Simsek Francesco Spadaro Clément Hongler Franck Gabriel 64 83 0 19 Feb 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 225 208 0 07 Feb 2020
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li Ruosong Wang Dingli Yu S. Du Wei Hu Ruslan Salakhutdinov Sanjeev Arora 66 132 0 03 Nov 2019
Ridge Regression: Structure, Cross-Validation, and Sketching Sifan Liu Yan Sun CML 65 48 0 06 Oct 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 83 635 0 14 Aug 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 226 925 0 26 Apr 2019
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data Khalil Elkhalil A. Kammoun Xiangliang Zhang Mohamed-Slim Alouini Tareq Al-Naffouri 42 7 0 19 Apr 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance Ulysse Marteau-Ferey Dmitrii Ostrovskii Francis R. Bach Alessandro Rudi 153 52 0 08 Feb 2019
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 221 1,272 0 04 Oct 2018
A Random Matrix Approach to Neural Networks Cosme Louart Zhenyu Liao Romain Couillet 65 161 0 17 Feb 2017
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification Yan Sun Stefan Wager 108 287 0 10 Jul 2015
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates Yuchen Zhang John C. Duchi Martin J. Wainwright 323 378 0 22 May 2013
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 161 282 0 09 Aug 2012
Group Invariant Scattering S. Mallat 123 991 0 12 Jan 2011
The spectrum of kernel random matrices N. Karoui 162 224 0 04 Jan 2010

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