The following pages link to Sums and Gaussian vectors (Q1903886):
Displaying 24 items.
- Gärtner-Ellis condition for squared asymptotically stationary Gaussian processes (Q340784) (← links)
- Moderate deviations via cumulants (Q354766) (← links)
- Integral operator approach to learning theory with unbounded sampling (Q371679) (← links)
- Consistent learning by composite proximal thresholding (Q681492) (← links)
- Elastic-net regularization in learning theory (Q1023403) (← links)
- A class of optimal estimators for the covariance operator in reproducing kernel Hilbert spaces (Q1755120) (← links)
- Optimal bounds in non-Gaussian limit theorems for \(U\)-statistics (Q1807184) (← links)
- Optimal convergence rates of high order Parzen windows with unbounded sampling (Q2251679) (← links)
- Large ball probabilities, Gaussian comparison and anti-concentration (Q2325333) (← links)
- Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods (Q2325378) (← links)
- Asymptotic behaviors of semidefinite programming with a covariance perturbation (Q2329680) (← links)
- Minkowski sums and Brownian exit times (Q2475500) (← links)
- Sample average approximations of strongly convex stochastic programs in Hilbert spaces (Q2688927) (← links)
- Low Rank Estimation of Similarities on Graphs (Q2840345) (← links)
- Concentration Inequalities for Statistical Inference (Q3380883) (← links)
- Consistency of kernel-based quantile regression (Q3552621) (← links)
- CROSS-VALIDATION BASED ADAPTATION FOR REGULARIZATION OPERATORS IN LEARNING THEORY (Q3560100) (← links)
- Regularized learning schemes in feature Banach spaces (Q4594821) (← links)
- Minimax Estimation of Kernel Mean Embeddings (Q4636999) (← links)
- Computing summing norms and type constants on few vectors (Q4713450) (← links)
- Distributed least squares prediction for functional linear regression* (Q5019925) (← links)
- Regularization: From Inverse Problems to Large-Scale Machine Learning (Q5028166) (← links)
- Estimation of scale functions to model heteroscedasticity by regularised kernel-based quantile methods (Q5419463) (← links)
- Learning particle swarming models from data with Gaussian processes (Q6562843) (← links)
