Simple proof of the risk bound for denoising by exponential weights for asymmetric noise distributions

DOI10.3103/s106836232306002xarXiv2212.12950MaRDI QIDQ6193809

Arnak S. Dalalyan

Publication date: 19 March 2024

Published in: Journal of Contemporary Mathematical Analysis. Armenian Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2212.12950

zbMATH Keywords

signal denoisingexponential weightsPAC-Bayesian bounds

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Cites Work

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• Ordered smoothers with exponential weighting
• Sparse regression learning by aggregation and Langevin Monte-Carlo
• Exponential screening and optimal rates of sparse estimation
• Learning by mirror averaging
• Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
• Minimax multiple shrinkage estimation
• Aggregating regression procedures to improve performance
• Combining different procedures for adaptive regression
• Optimal bounds for aggregation of affine estimators
• Sharp oracle inequalities for aggregation of affine estimators
• PAC-Bayesian bounds for sparse regression estimation with exponential weights
• On the optimality of the aggregate with exponential weights for low temperatures
• Exponential weights in multivariate regression and a low-rankness favoring prior
• Aggregation for Gaussian regression
• Information Theory and Mixing Least-Squares Regressions
• Combining Minimax Shrinkage Estimators
• High-Dimensional Probability
• Aggregation by Exponential Weighting and Sharp Oracle Inequalities