Kernel regression in high dimensions: Refined analysis beyond double descent

6 October 2020

Papers citing "Kernel regression in high dimensions: Refined analysis beyond double descent"

16 / 16 papers shown

Title
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum Tin Sum Cheng Aurelien Lucchi Anastasis Kratsios David Belius 45 8 0 02 Feb 2024
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay Yicheng Li Weiye Gan Zuoqiang Shi Qian Lin 41 5 0 03 Jan 2024
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 42 9 0 18 Jan 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 20 5 0 14 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 37 31 0 30 Sep 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 17 28 0 23 Feb 2022
A Generalized Weighted Optimization Method for Computational Learning and Inversion Bjorn Engquist Kui Ren Yunan Yang 31 4 0 23 Jan 2022
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks Zhichao Wang Yizhe Zhu 37 18 0 20 Sep 2021
Towards an Understanding of Benign Overfitting in Neural Networks Zhu Li Zhi-Hua Zhou Arthur Gretton MLT 33 35 0 06 Jun 2021
Adversarially Robust Kernel Smoothing Jia-Jie Zhu Christina Kouridi Yassine Nemmour Bernhard Schölkopf 28 7 0 16 Feb 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 35 136 0 16 Feb 2021
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 35 61 0 03 Aug 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 149 201 0 07 Feb 2020
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 86 280 0 09 Aug 2012
Elastic-Net Regularization in Learning Theory C. D. Mol Ernesto De Vito Lorenzo Rosasco OOD CML 110 306 0 22 Jul 2008