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Optimal Rates for Spectral Algorithms with Least-Squares Regression over
  Hilbert Spaces
v1v2v3v4 (latest)

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

20 January 2018
Junhong Lin
Alessandro Rudi
Lorenzo Rosasco
Volkan Cevher
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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?
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
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
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
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
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
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
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
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
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
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
Distributed Learning with Dependent Samples
Zirui Sun
Shao-Bo Lin
53
7
0
10 Feb 2020
Beating SGD Saturation with Tail-Averaging and Minibatching
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
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
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
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
Learning with SGD and Random Features
Luigi Carratino
Alessandro Rudi
Lorenzo Rosasco
86
78
0
17 Jul 2018
Manifold Structured Prediction
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
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
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
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
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
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm
Simon Fischer
Ingo Steinwart
206
152
0
23 Feb 2017
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