Pages that link to "Item:Q2373576"
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The following pages link to Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576):
Displaying 25 items.
- Complexity versus Agreement for Many Views (Q3648755) (← links)
- (Q4558184) (← links)
- Optimal survey schemes for stochastic gradient descent with applications to <i>M</i>-estimation (Q4967801) (← links)
- (Q4969103) (← links)
- (Q4998937) (← links)
- (Q4998979) (← links)
- (Q5053241) (← links)
- HONEST CONFIDENCE SETS IN NONPARAMETRIC IV REGRESSION AND OTHER ILL-POSED MODELS (Q5118575) (← links)
- (Q5149046) (← links)
- Sampling and empirical risk minimization (Q5276167) (← links)
- Concentration Inequalities for Samples without Replacement (Q5369328) (← links)
- Variance-based regularization with convex objectives (Q5381122) (← links)
- <i>U</i>-Processes and Preference Learning (Q5383812) (← links)
- (Q5879931) (← links)
- Deep learning: a statistical viewpoint (Q5887827) (← links)
- Sample average approximation with heavier tails. I: Non-asymptotic bounds with weak assumptions and stochastic constraints (Q6038637) (← links)
- Sample average approximation with heavier tails II: localization in stochastic convex optimization and persistence results for the Lasso (Q6038638) (← links)
- Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data (Q6058524) (← links)
- Wild bootstrap inference for penalized quantile regression for longitudinal data (Q6108328) (← links)
- Convergence rates for shallow neural networks learned by gradient descent (Q6137712) (← links)
- Robust supervised learning with coordinate gradient descent (Q6172182) (← links)
- Locally simultaneous inference (Q6608687) (← links)
- Sample average approximation for stochastic programming with equality constraints (Q6622760) (← links)
- Concentration bounds for the empirical angular measure with statistical learning applications (Q6635715) (← links)
- Solving PDEs on spheres with physics-informed convolutional neural networks (Q6652574) (← links)
