A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids
type: Item
instance of: scholarly article
title: A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids
DOI: 10.1214/22-AOS2212
author: Tianyu Zhang
author: Noah Simon
published in: The Annals of Statistics
publication date: 2022-12-08T00:00:00Z
full work available at URL: https://arxiv.org/abs/2104.00846
Mathematics Subject Classification ID: 62G08
zbMATH DE Number: 7628843
zbMATH Keywords: stochastic gradient descent
zbMATH Keywords: nonparametric regression
zbMATH Keywords: online learning
describes a project that uses: GaussQR
describes a project that uses: gamair
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
cites work: Computational Complexity
cites work: Reconciling modern machine-learning practice and the classical bias–variance trade-off
cites work: Q5488485
cites work: Large-Scale Machine Learning with Stochastic Gradient Descent
cites work: Computational and statistical boundaries for submatrix localization in a large noisy matrix
cites work: Optimal rates for the regularized least-squares algorithm
cites work: Support Vector Machines
cites work: On the mathematical foundations of learning
cites work: Nonparametric stochastic approximation with large step-sizes
cites work: Curve fitting by polynomial-trigonometric regression
cites work: Kernel-based Approximation Methods using MATLAB
cites work: Minimax estimation in sparse canonical correlation analysis
cites work: Sparse CCA: adaptive estimation and computational barriers
cites work: Q4944751
cites work: A distribution-free theory of nonparametric regression
cites work: Theory for penalised spline regression
cites work: Q4896587
cites work: Q3265072
cites work: Q4633012
cites work: Q4421713
cites work: Just interpolate: kernel ``ridgeless'' regression can generalize
cites work: Large scale online kernel learning
cites work: Computational barriers in minimax submatrix detection
cites work: Q4511091
cites work: Q3967358
cites work: Tractability of multivariate problems. Volume I: Linear information
cites work: On Martingale Extensions of Vapnik–Chervonenkis Theory with Applications to Online Learning
cites work: Mercer's theorem on general domains: on the interaction between measures, kernels, and RKHSs
cites work: Optimal rates of convergence for nonparametric estimators
cites work: Additive regression and other nonparametric models
cites work: Mercer theorem for RKHS on noncompact sets
cites work: Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence
cites work: Q3996207
cites work: High-Dimensional Statistics
cites work: Statistical and computational trade-offs in estimation of sparse principal components
cites work: Q5268958
cites work: Online gradient descent learning algorithms
cites work: A reproducing kernel Hilbert space approach to functional linear regression
cites work: Minimax optimal rates of estimation in high dimensional additive models
cites work: An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces
description: scientific article; zbMATH DE number 7628843