Performance of empirical risk minimization in linear aggregation
From MaRDI portal
Publication:282546
DOI10.3150/15-BEJ701zbMath1346.60075arXiv1402.5763OpenAlexW3103224927MaRDI QIDQ282546
Shahar Mendelson, Guillaume Lecué
Publication date: 12 May 2016
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1402.5763
Related Items
On least squares estimation under heteroscedastic and heavy-tailed errors, On aggregation for heavy-tailed classes, Robust statistical learning with Lipschitz and convex loss functions, Unnamed Item, A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning, Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression, Regularization and the small-ball method II: complexity dependent error rates, An elementary analysis of ridge regression with random design, Mean estimation and regression under heavy-tailed distributions: A survey, Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices, Suboptimality of constrained least squares and improvements via non-linear predictors, Distribution-free robust linear regression
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sparse recovery under weak moment assumptions
- Sharper lower bounds on the performance of the empirical risk minimization algorithm
- Robust linear least squares regression
- Linear and convex aggregation of density estimators
- Aggregation via empirical risk minimization
- Learning by mirror averaging
- Lectures on probability theory and statistics. Ecole d'Eté de probabilités de Saint-Flour XXV - 1995. Lectures given at the summer school in Saint-Flour, France, July 10-26, 1995
- Aggregating regression procedures to improve performance
- Mixing strategies for density estimation.
- Functional aggregation for nonparametric regression.
- Statistical learning theory and stochastic optimization. Ecole d'Eté de Probabilitiés de Saint-Flour XXXI -- 2001.
- Weak convergence and empirical processes. With applications to statistics
- Empirical risk minimization is optimal for the convex aggregation problem
- Optimal learning with \textit{Q}-aggregation
- Aggregation for Gaussian regression
- Minimax rate of convergence and the performance of empirical risk minimization in phase recovery
- Learning without Concentration
- Bounding the Smallest Singular Value of a Random Matrix Without Concentration
- Support Vector Machines
- Adaptive Regression by Mixing
- Neural Network Learning
- A Remark on the Diameter of Random Sections of Convex Bodies
- Learning Theory and Kernel Machines
