Pages that link to "Item:Q2385535"
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The following pages link to Optimal rates for the regularized least-squares algorithm (Q2385535):
Displaying 50 items.
- Distributed regularized least squares with flexible Gaussian kernels (Q2036424) (← links)
- Linearized two-layers neural networks in high dimension (Q2039801) (← links)
- Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space (Q2076148) (← links)
- Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions (Q2077623) (← links)
- An elementary analysis of ridge regression with random design (Q2080945) (← links)
- Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices (Q2091833) (← links)
- Functional linear regression with Huber loss (Q2099272) (← links)
- Approximate kernel PCA: computational versus statistical trade-off (Q2105193) (← links)
- A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids (Q2105198) (← links)
- Distribution-free robust linear regression (Q2113267) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- Online gradient descent algorithms for functional data learning (Q2121498) (← links)
- State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings (Q2123214) (← links)
- Non-asymptotic error bound for optimal prediction of function-on-function regression by RKHS approach (Q2131156) (← links)
- Deep learning for inverse problems. Abstracts from the workshop held March 7--13, 2021 (hybrid meeting) (Q2131206) (← links)
- Generalization error of random feature and kernel methods: hypercontractivity and kernel matrix concentration (Q2134105) (← links)
- Learning rate of distribution regression with dependent samples (Q2171946) (← links)
- Manifold regularization based on Nyström type subsampling (Q2175018) (← links)
- Distributed kernel gradient descent algorithm for minimum error entropy principle (Q2175022) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems (Q2191842) (← links)
- Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems (Q2192321) (← links)
- Just interpolate: kernel ``ridgeless'' regression can generalize (Q2196223) (← links)
- ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels (Q2209821) (← links)
- Finite-sample analysis of \(M\)-estimators using self-concordance (Q2219231) (← links)
- Kernel gradient descent algorithm for information theoretic learning (Q2223567) (← links)
- Finite sample performance of linear least squares estimation (Q2235406) (← links)
- Almost optimal estimates for approximation and learning by radial basis function networks (Q2251472) (← links)
- Optimal convergence rates of high order Parzen windows with unbounded sampling (Q2251679) (← links)
- Learning sets with separating kernels (Q2252512) (← links)
- Least-square regularized regression with non-iid sampling (Q2272113) (← links)
- Balancing principle in supervised learning for a general regularization scheme (Q2278452) (← links)
- Optimal learning rates for distribution regression (Q2283125) (← links)
- Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces (Q2300763) (← links)
- Moving quantile regression (Q2301045) (← links)
- Fast and strong convergence of online learning algorithms (Q2305549) (← links)
- High-probability bounds for the reconstruction error of PCA (Q2307416) (← links)
- On nonparametric randomized sketches for kernels with further smoothness (Q2322682) (← links)
- Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods (Q2325378) (← links)
- Optimal rates for coefficient-based regularized regression (Q2330932) (← links)
- Multi-task learning via linear functional strategy (Q2407408) (← links)
- Distributed learning with multi-penalty regularization (Q2415399) (← links)
- On randomized trace estimates for indefinite matrices with an application to determinants (Q2671299) (← links)
- Nonasymptotic analysis of robust regression with modified Huber's loss (Q2693696) (← links)
- Low-rank kernel approximation of Lyapunov functions using neural networks (Q2696116) (← links)
- Convergence analysis of online learning algorithm with two-stage step size (Q2698633) (← links)
- A Vector-Contraction Inequality for Rademacher Complexities (Q2830263) (← links)
- Convergence rates of kernel conjugate gradient for random design regression (Q2835985) (← links)
- On extension theorems and their connection to universal consistency in machine learning (Q2835986) (← links)
- Indefinite kernel network with dependent sampling (Q2855474) (← links)
