Pages that link to "Item:Q1688423"
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The following pages link to On concentration for (regularized) empirical risk minimization (Q1688423):
Displaying 18 items.
- An improved global risk bound in concave regression (Q309525) (← links)
- Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces (Q550498) (← links)
- Learning without concentration for general loss functions (Q1647935) (← links)
- Discussion of ``On concentration for (regularized) empirical risk minimization'' by Sara van de Geer and Martin Wainwright (Q1688424) (← links)
- Discussion of the paper ``On concentration for (regularized) empirical risk minimization'' (Q1688425) (← links)
- Nonparametric shape-restricted regression (Q1730903) (← links)
- Sharp oracle inequalities for least squares estimators in shape restricted regression (Q1750286) (← links)
- Penalized empirical risk minimization over Besov spaces (Q1952004) (← links)
- Set structured global empirical risk minimizers are rate optimal in general dimensions (Q2054522) (← links)
- On least squares estimation under heteroscedastic and heavy-tailed errors (Q2119229) (← links)
- High-dimensional CLT: improvements, non-uniform extensions and large deviations (Q2214242) (← links)
- Minimax rate of convergence and the performance of empirical risk minimization in phase recovery (Q2515924) (← links)
- On tight bounds for the Lasso (Q4558195) (← links)
- Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases (Q4578060) (← links)
- On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers (Q5271794) (← links)
- Concentration inequalities of the cross-validation estimator for empirical risk minimizer (Q5276169) (← links)
- Concentration behavior of the penalized least squares estimator (Q6089165) (← links)
- Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions (Q6183752) (← links)
