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Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

20 January 2018

Papers citing "Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces"

36 / 36 papers shown

Title
Learning Curves of Stochastic Gradient Descent in Kernel Regression Haihan Zhang Weicheng Lin Yuanshi Liu Cong Fang 38 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
Divergence of Empirical Neural Tangent Kernel in Classification Problems Zixiong Yu Songtao Tian Guhan Chen 70 0 0 15 Apr 2025
On the Pinsker bound of inner product kernel regression in large dimensions Weihao Lu Jialin Ding Haobo Zhang Qian Lin 93 1 0 02 Sep 2024
Random feature approximation for general spectral methods Mike Nguyen Nicole Mücke 52 1 0 29 Aug 2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization Vivien A. Cabannes B. Kiani Randall Balestriero Yann LeCun A. Bietti SSL 92 33 0 06 Feb 2023
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 85 12 0 20 Nov 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 90 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 49 6 0 08 Jun 2022
A Case of Exponential Convergence Rates for SVM Vivien A. Cabannes Stefano Vigogna 75 2 0 20 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 105 8 0 15 May 2022
Dimensionality Reduction and Wasserstein Stability for Kernel Regression Stephan Eckstein Armin Iske Mathias Trabs 42 4 0 17 Mar 2022
Radial Basis Function Approximation with Distributively Stored Data on Spheres Han Feng Shao-Bo Lin Ding-Xuan Zhou 32 8 0 05 Dec 2021
Data splitting improves statistical performance in overparametrized regimes Nicole Mücke Enrico Reiss Jonas Rungenhagen Markus Klein 55 8 0 21 Oct 2021
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? Dominic Richards Yan Sun Patrick Rebeschini 74 3 0 26 Aug 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti Ernesto De Vito Matti Lassas Luca Ratti Matteo Santacesaria 69 30 0 11 Jun 2021
From inexact optimization to learning via gradient concentration Bernhard Stankewitz Nicole Mücke Lorenzo Rosasco 88 5 0 09 Jun 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui Bruno Loureiro Florent Krzakala Lenka Zdeborová 92 85 0 31 May 2021
Sobolev Norm Learning Rates for Conditional Mean Embeddings Prem M. Talwai A. Shameli D. Simchi-Levi 87 11 0 16 May 2021
Online nonparametric regression with Sobolev kernels O. Zadorozhnyi Pierre Gaillard Sébastien Gerchinovitz Alessandro Rudi 47 4 0 06 Feb 2021
Fast rates in structured prediction Vivien A. Cabannes Alessandro Rudi Francis R. Bach 423 19 0 01 Feb 2021
Nonparametric approximation of conditional expectation operators Mattes Mollenhauer P. Koltai 93 17 0 23 Dec 2020
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime Atsushi Nitanda Taiji Suzuki 81 41 0 22 Jun 2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms Alain Celisse Martin Wahl 65 18 0 17 Apr 2020
Distributed Learning with Dependent Samples Zirui Sun Shao-Bo Lin 53 7 0 10 Feb 2020
Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Mücke Gergely Neu Lorenzo Rosasco 106 37 0 22 Feb 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance Ulysse Marteau-Ferey Dmitrii Ostrovskii Francis R. Bach Alessandro Rudi 188 52 0 08 Feb 2019
Kernel Conjugate Gradient Methods with Random Projections Bailey Kacsmar Douglas R Stinson 56 4 0 05 Nov 2018
On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi Daniele Calandriello Luigi Carratino Lorenzo Rosasco 83 82 0 31 Oct 2018
Learning with SGD and Random Features Luigi Carratino Alessandro Rudi Lorenzo Rosasco 86 78 0 17 Jul 2018
Manifold Structured Prediction Alessandro Rudi C. Ciliberto Gian Maria Marconi Lorenzo Rosasco 147 18 0 26 Jun 2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance Giulia Luise Alessandro Rudi Massimiliano Pontil C. Ciliberto OT 91 133 0 30 May 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes Loucas Pillaud-Vivien Alessandro Rudi Francis R. Bach 179 103 0 25 May 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces Junhong Lin Volkan Cevher 30 9 0 12 Mar 2018
Early stopping for kernel boosting algorithms: A general analysis with localized complexities Yuting Wei Fanny Yang Martin J. Wainwright 90 77 0 05 Jul 2017
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm Simon Fischer Ingo Steinwart 206 152 0 23 Feb 2017