Pages that link to "Item:Q549116"
From MaRDI portal
The following pages link to Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008. (Q549116):
Displaying 50 items.
- Geometric median and robust estimation in Banach spaces (Q122792) (← links)
- Censored linear model in high dimensions. Penalised linear regression on high-dimensional data with left-censored response variable (Q285835) (← links)
- Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model (Q297474) (← links)
- A rank-corrected procedure for matrix completion with fixed basis coefficients (Q312678) (← links)
- Estimation of low rank density matrices: bounds in Schatten norms and other distances (Q315410) (← links)
- Low rank estimation of smooth kernels on graphs (Q355091) (← links)
- Kullback-Leibler aggregation and misspecified generalized linear models (Q447818) (← links)
- General nonexact oracle inequalities for classes with a subexponential envelope (Q447832) (← links)
- Von Neumann entropy penalization and low-rank matrix estimation (Q449975) (← links)
- Optimal exponential bounds on the accuracy of classification (Q485316) (← links)
- \(L_1\)-penalization in functional linear regression with subgaussian design (Q487731) (← links)
- Cox process functional learning (Q500875) (← links)
- Dimensionality reduction with subgaussian matrices: a unified theory (Q515989) (← links)
- Sparse recovery under weak moment assumptions (Q520739) (← links)
- Oracle inequalities and optimal inference under group sparsity (Q651028) (← links)
- Concentration inequalities for matrix martingales in continuous time (Q681530) (← links)
- Robust matrix completion (Q682808) (← links)
- On oracle inequalities related to data-driven hard thresholding (Q718892) (← links)
- High-dimensional model recovery from random sketched data by exploring intrinsic sparsity (Q782446) (← links)
- Optimal prediction of quantile functional linear regression in reproducing kernel Hilbert spaces (Q826973) (← links)
- Sparsity in penalized empirical risk minimization (Q838303) (← links)
- Matrix completion by singular value thresholding: sharp bounds (Q887273) (← links)
- Approximating polyhedra with sparse inequalities (Q896289) (← links)
- Tail index estimation, concentration and adaptivity (Q902214) (← links)
- On the prediction loss of the Lasso in the partially labeled setting (Q1616320) (← links)
- Learning without concentration for general loss functions (Q1647935) (← links)
- Localization of VC classes: beyond local Rademacher complexities (Q1663641) (← links)
- Local Rademacher complexity: sharper risk bounds with and without unlabeled samples (Q1669081) (← links)
- On concentration for (regularized) empirical risk minimization (Q1688423) (← links)
- Discussion of ``On concentration for (regularized) empirical risk minimization'' by Sara van de Geer and Martin Wainwright (Q1688424) (← links)
- Estimating a network from multiple noisy realizations (Q1711599) (← links)
- Bayesian fractional posteriors (Q1731743) (← links)
- Oracle inequalities for high-dimensional prediction (Q1740524) (← links)
- Concentration of the empirical level sets of Tukey's halfspace depth (Q1740596) (← links)
- Regularization and the small-ball method. I: Sparse recovery (Q1750281) (← links)
- Some lower bounds on sparse outer approximations of polytopes (Q1785369) (← links)
- On the exponentially weighted aggregate with the Laplace prior (Q1800807) (← links)
- Statistical learning theory and stochastic optimization. Ecole d'Eté de Probabilitiés de Saint-Flour XXXI -- 2001. (Q1880598) (← links)
- On the optimality of the empirical risk minimization procedure for the convex aggregation problem (Q1943331) (← links)
- Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces (Q1979424) (← links)
- Slope meets Lasso: improved oracle bounds and optimality (Q1990596) (← links)
- Estimation from nonlinear observations via convex programming with application to bilinear regression (Q2002578) (← links)
- Surrogate losses in passive and active learning (Q2008623) (← links)
- Model selection in utility-maximizing binary prediction (Q2024476) (← links)
- Sharpness estimation of combinatorial generalization ability bounds for threshold decision rules (Q2034842) (← links)
- Minimax estimation of smooth optimal transport maps (Q2039809) (← links)
- Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection (Q2056283) (← links)
- Quantile trace regression via nuclear norm regularization (Q2070597) (← links)
- Analysis of generalized Bregman surrogate algorithms for nonsmooth nonconvex statistical learning (Q2073715) (← links)
- A generalized Catoni's M-estimator under finite \(\alpha\)-th moment assumption with \(\alpha \in (1,2)\) (Q2074299) (← links)
