Optimal learning with \textit{Q}-aggregation
From MaRDI portal
Publication:2448729
DOI10.1214/13-AOS1190zbMath1286.68255arXiv1301.6080OpenAlexW3099410262MaRDI QIDQ2448729
Philippe Rigollet, Guillaume Lecué
Publication date: 5 May 2014
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.6080
Nonparametric regression and quantile regression (62G08) Computational learning theory (68Q32) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (10)
Performance of empirical risk minimization in linear aggregation ⋮ Unnamed Item ⋮ Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control ⋮ Targeting underrepresented populations in precision medicine: a federated transfer learning approach ⋮ An adaptive multiclass nearest neighbor classifier ⋮ Aggregation of affine estimators ⋮ Optimal bounds for aggregation of affine estimators ⋮ Optimal learning with Bernstein Online Aggregation ⋮ Localized Gaussian width of \(M\)-convex hulls with applications to Lasso and convex aggregation ⋮ Aggregating estimates by convex optimization
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sparse regression learning by aggregation and Langevin Monte-Carlo
- Mirror averaging with sparsity priors
- Kullback-Leibler aggregation and misspecified generalized linear models
- Exponential screening and optimal rates of sparse estimation
- Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
- Sharper lower bounds on the performance of the empirical risk minimization algorithm
- Deviation optimal learning using greedy \(Q\)-aggregation
- Aggregation via empirical risk minimization
- Learning by mirror averaging
- On concentration of self-bounding functions
- Combining different procedures for adaptive regression
- Mixing strategies for density estimation.
- Functional aggregation for nonparametric regression.
- Optimal aggregation of classifiers in statistical learning.
- Sharp oracle inequalities for aggregation of affine estimators
- PAC-Bayesian bounds for sparse regression estimation with exponential weights
- Fast learning rates in statistical inference through aggregation
- Statistical inference in compound functional models
- Aggregation for Gaussian regression
- Optimal rates of aggregation in classification under low noise assumption
- Learning Theory and Kernel Machines
- Aggregation by Exponential Weighting and Sharp Oracle Inequalities
- Suboptimality of Penalized Empirical Risk Minimization in Classification
- Convexity, Classification, and Risk Bounds
- Sparse estimation by exponential weighting
This page was built for publication: Optimal learning with \textit{Q}-aggregation