scientific article; zbMATH DE number 7306853
From MaRDI portal
Publication:5148925
Publication date: 5 February 2021
Full work available at URL: https://arxiv.org/abs/1801.07226
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items (4)
Spectral algorithms for learning with dependent observations ⋮ Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces ⋮ Distributed learning with indefinite kernels ⋮ Unnamed Item
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonparametric stochastic approximation with large step-sizes
- Random design analysis of ridge regression
- Kernel ridge vs. principal component regression: minimax bounds and the qualification of regularization operators
- Optimal rates for regularization of statistical inverse learning problems
- User-friendly tail bounds for sums of random matrices
- On regularization algorithms in learning theory
- Online gradient descent learning algorithms
- Randomized sketches for kernels: fast and optimal nonparametric regression
- Optimal rates for the regularized least-squares algorithm
- On some extensions of Bernstein's inequality for self-adjoint operators
- Learning rates of least-square regularized regression
- Online learning algorithms
- Boosting with early stopping: convergence and consistency
- Learning theory estimates via integral operators and their approximations
- On early stopping in gradient descent learning
- Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
- Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence
- Learning Theory
- Support Vector Machines
- Spectral Algorithms for Supervised Learning
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- On the Generalization Ability of On-Line Learning Algorithms
- CROSS-VALIDATION BASED ADAPTATION FOR REGULARIZATION OPERATORS IN LEARNING THEORY
- A new concentration result for regularized risk minimizers
- Robust Stochastic Approximation Approach to Stochastic Programming
- Remarks on Inequalities for Large Deviation Probabilities
- Acceleration of Stochastic Approximation by Averaging
- Norm Inequalities Equivalent to Heinz Inequality
- Practical Sketching Algorithms for Low-Rank Matrix Approximation
- Optimal Rates for Multi-pass Stochastic Gradient Methods
- Optimization Methods for Large-Scale Machine Learning
- Learning theory of distributed spectral algorithms
- Learning Bounds for Kernel Regression Using Effective Data Dimensionality
- A Stochastic Approximation Method
This page was built for publication: