Pages that link to "Item:Q2373576"

The following pages link to Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576):

Displaying 50 items.

• Optimal linear discriminators for the discrete choice model in growing dimensions (Q2073710) (← links)
• An elementary analysis of ridge regression with random design (Q2080945) (← links)
• Suboptimality of constrained least squares and improvements via non-linear predictors (Q2108490) (← links)
• A no-free-lunch theorem for multitask learning (Q2112800) (← links)
• On least squares estimation under heteroscedastic and heavy-tailed errors (Q2119229) (← links)
• Empirical variance minimization with applications in variance reduction and optimal control (Q2137023) (← links)
• Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms (Q2159256) (← links)
• Robust statistical learning with Lipschitz and convex loss functions (Q2174664) (← links)
• Convergence rates for empirical barycenters in metric spaces: curvature, convexity and extendable geodesics (Q2182123) (← links)
• On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces (Q2185697) (← links)
• Aggregation of estimators and stochastic optimization (Q2197367) (← links)
• From Gauss to Kolmogorov: localized measures of complexity for ellipses (Q2199701) (← links)
• ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels (Q2209821) (← links)
• Nonparametric regression using deep neural networks with ReLU activation function (Q2215715) (← links)
• Performance guarantees for policy learning (Q2227481) (← links)
• Concentration inequalities for two-sample rank processes with application to bipartite ranking (Q2233587) (← links)
• A local Vapnik-Chervonenkis complexity (Q2281678) (← links)
• Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions (Q2313281) (← links)
• Convergence rates of least squares regression estimators with heavy-tailed errors (Q2313287) (← links)
• Inference on covariance operators via concentration inequalities: \(k\)-sample tests, classification, and clustering via Rademacher complexities (Q2317001) (← links)
• Localized Gaussian width of \(M\)-convex hulls with applications to Lasso and convex aggregation (Q2325349) (← links)
• Rademacher complexity for Markov chains: applications to kernel smoothing and Metropolis-Hastings (Q2325397) (← links)
• Nonparametric estimation of low rank matrix valued function (Q2326073) (← links)
• Nonasymptotic bounds for vector quantization in Hilbert spaces (Q2343956) (← links)
• Minimax fast rates for discriminant analysis with errors in variables (Q2345118) (← links)
• Statistical properties of kernel principal component analysis (Q2384134) (← links)
• Model selection by bootstrap penalization for classification (Q2384135) (← links)
• Fast learning rates in statistical inference through aggregation (Q2388975) (← links)
• Statistical performance of support vector machines (Q2426613) (← links)
• Ranking and empirical minimization of \(U\)-statistics (Q2426626) (← links)
• Global uniform risk bounds for wavelet deconvolution estimators (Q2429928) (← links)
• Rates of convergence in active learning (Q2429933) (← links)
• Empirical risk minimization is optimal for the convex aggregation problem (Q2435238) (← links)
• Minimax adaptive dimension reduction for regression (Q2451628) (← links)
• Aggregation for Gaussian regression (Q2456016) (← links)
• Simultaneous adaptation to the margin and to complexity in classification (Q2456017) (← links)
• Empirical minimization (Q2494402) (← links)
• Concentration inequalities and asymptotic results for ratio type empirical processes (Q2497173) (← links)
• Bandwidth selection in kernel empirical risk minimization via the gradient (Q2515491) (← links)
• Square root penalty: Adaption to the margin in classification and in edge estimation (Q2569239) (← links)
• Complexities of convex combinations and bounding the generalization error in classification (Q2583410) (← links)
• Classifiers of support vector machine type with \(\ell_1\) complexity regularization (Q2642804) (← links)
• Complex sampling designs: uniform limit theorems and applications (Q2656604) (← links)
• Compressive statistical learning with random feature moments (Q2664824) (← links)
• Gibbs posterior concentration rates under sub-exponential type losses (Q2692523) (← links)
• Nonasymptotic analysis of robust regression with modified Huber's loss (Q2693696) (← links)
• On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer (Q3085585) (← links)
• Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
• Noisy discriminant analysis with boundary assumptions (Q3455256) (← links)
• FAST RATES FOR ESTIMATION ERROR AND ORACLE INEQUALITIES FOR MODEL SELECTION (Q3632389) (← links)