Optimal Rates For Regularization Of Statistical Inverse Learning Problems

14 April 2016

Papers citing "Optimal Rates For Regularization Of Statistical Inverse Learning Problems"

50 / 98 papers shown

Title
Learning Curves of Stochastic Gradient Descent in Kernel Regression Haihan Zhang Weicheng Lin Yuanshi Liu Cong Fang 40 0 0 28 May 2025
Regularized least squares learning with heavy-tailed noise is minimax optimal Mattes Mollenhauer Nicole Mücke Dimitri Meunier Arthur Gretton 91 0 0 20 May 2025
Sobolev norm inconsistency of kernel interpolation Yunfei Yang 111 0 0 29 Apr 2025
A Lipschitz spaces view of infinitely wide shallow neural networks Francesca Bartolucci Marcello Carioni José A. Iglesias Yury Korolev Emanuele Naldi Stefano Vigogna 112 1 0 18 Oct 2024
Diffusion-based Semi-supervised Spectral Algorithm for Regression on Manifolds Weichun Xia Jiaxin Jiang Lei Shi 54 0 0 18 Oct 2024
Distributed Learning with Discretely Observed Functional Data Jiading Liu Lei Shi 58 0 0 03 Oct 2024
$Gaussian kernel expansion with basis functions uniformly bounded in $\mathcal{L}_{\infty}$$ Gaussian kernel expansion with basis functions uniformly bounded in $\mathcal{L}_{\infty}$ M. Bisiacco G. Pillonetto 64 0 0 02 Oct 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms Dimitri Meunier Zikai Shen Mattes Mollenhauer Arthur Gretton Zhu Li 127 5 0 23 May 2024
On the Saturation Effect of Kernel Ridge Regression Yicheng Li Haobo Zhang Qian Lin 164 21 0 15 May 2024
Improve Generalization Ability of Deep Wide Residual Network with A Suitable Scaling Factor Songtao Tian Zixiong Yu 50 1 0 07 Mar 2024
Spectral Algorithms on Manifolds through Diffusion Weichun Xia Lei Shi 49 1 0 06 Mar 2024
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization Lukas Gruber Markus Holzleitner Johannes Lehner Sepp Hochreiter Werner Zellinger 119 2 0 21 Feb 2024
Early stopping by correlating online indicators in neural networks M. Ferro V. Darriba Francisco J. Ribadas Pena Jesús Vilares 48 9 0 04 Feb 2024
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay Yicheng Li Weiye Gan Zuoqiang Shi Qian Lin 65 6 0 03 Jan 2024
Statistical inverse learning problems with random observations Abhishake T. Helin Nicole Mucke 66 1 0 23 Dec 2023
Learned reconstruction methods for inverse problems: sample error estimates Luca Ratti 62 0 0 21 Dec 2023
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 143 8 0 12 Dec 2023
Adaptive Parameter Selection for Kernel Ridge Regression Shao-Bo Lin 26 3 0 10 Dec 2023
A statistical perspective on algorithm unrolling models for inverse problems Yves Atchadé Xinru Liu Qiuyun Zhu 55 0 0 10 Nov 2023
Improved Convergence Rate of Nested Simulation with LSE on Sieve Ruoxue Liu Liang Ding Wei Cao Lu Zou 40 0 0 18 Oct 2023
Kernel-based function learning in dynamic and non stationary environments Alberto Giaretta M. Bisiacco G. Pillonetto 21 2 0 04 Oct 2023
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent Mike Nguyen Nicole Mücke MLT 84 6 0 14 Sep 2023
Random feature approximation for general spectral methods Mike Nguyen Nicole Mücke 60 1 0 29 Aug 2023
Adaptive learning of density ratios in RKHS Werner Zellinger S. Kindermann S. Pereverzyev 69 5 0 30 Jul 2023
Nonlinear Meta-Learning Can Guarantee Faster Rates Dimitri Meunier Zhu Li Arthur Gretton Samory Kpotufe 180 7 0 20 Jul 2023
Local Risk Bounds for Statistical Aggregation Jaouad Mourtada Tomas Vavskevivcius Nikita Zhivotovskiy 66 1 0 29 Jun 2023
Generalization Ability of Wide Residual Networks Jianfa Lai Zixiong Yu Songtao Tian Qian Lin 63 4 0 29 May 2023
On the Optimality of Misspecified Kernel Ridge Regression Haobo Zhang Yicheng Li Weihao Lu Qian Lin 122 14 0 12 May 2023
Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding Tianyang Hu Jiahan Jiang Donghao Li Wei Cao Yuan Yao 74 6 0 05 May 2023
Optimality of Robust Online Learning Zheng-Chu Guo A. Christmann Lei Shi 58 10 0 20 Apr 2023
Kernel interpolation generalizes poorly Yicheng Li Haobo Zhang Qian Lin 83 11 0 28 Mar 2023
On the Optimality of Misspecified Spectral Algorithms Hao Zhang Yicheng Li Qian Lin 79 18 0 27 Mar 2023
Sketching with Spherical Designs for Noisy Data Fitting on Spheres Shao-Bo Lin Di Wang Ding-Xuan Zhou 57 2 0 08 Mar 2023
$Generalization Ability of Wide Neural Networks on $\mathbb{R}$$ Generalization Ability of Wide Neural Networks on $\mathbb{R}$ Jianfa Lai Manyun Xu Rui Chen Qi-Rong Lin 92 23 0 12 Feb 2023
Statistical Learning with Sublinear Regret of Propagator Models Eyal Neuman Yufei Zhang 106 7 0 12 Jan 2023
A note on the prediction error of principal component regression in high dimensions L. Hucker Martin Wahl 88 6 0 09 Dec 2022
Least squares approximations in linear statistical inverse learning problems T. Helin 45 2 0 22 Nov 2022
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 92 12 0 20 Nov 2022
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem Mattes Mollenhauer Nicole Mücke T. Sullivan 96 26 0 16 Nov 2022
Importance Weighting Correction of Regularized Least-Squares for Covariate and Target Shifts Davit Gogolashvili OOD 63 1 0 18 Oct 2022
Statistical Inverse Problems in Hilbert Scales Abhishake Rastogi 31 3 0 28 Aug 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 97 52 0 02 Aug 2022
Functional linear and single-index models: A unified approach via Gaussian Stein identity Krishnakumar Balasubramanian Hans-Georg Müller Bharath K. Sriperumbudur 63 6 0 08 Jun 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 107 8 0 15 May 2022
Optimal Learning Rates for Regularized Least-Squares with a Fourier Capacity Condition Prem M. Talwai D. Simchi-Levi 35 2 0 16 Apr 2022
An elementary analysis of ridge regression with random design Jaouad Mourtada Lorenzo Rosasco 82 11 0 16 Mar 2022
On the Benefits of Large Learning Rates for Kernel Methods Gaspard Beugnot Julien Mairal Alessandro Rudi 82 11 0 28 Feb 2022
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions Wei Cao Yanyuan Wang Xiaowei Zhang 46 5 0 09 Jan 2022
Shearlet-based regularization in statistical inverse learning with an application to X-ray tomography T. Bubba Luca Ratti 59 3 0 23 Dec 2021
Learning curves for Gaussian process regression with power-law priors and targets Hui Jin P. Banerjee Guido Montúfar 72 18 0 23 Oct 2021