Structured Signal Recovery From Non-Linear and Heavy-Tailed Measurements

DOI10.1109/TIT.2018.2842216zbMath1401.94035arXiv1609.01025OpenAlexW2963047451WikidataQ129746257 ScholiaQ129746257MaRDI QIDQ4682858

Xiaohan Wei, Stanislav Minsker, Larry Goldstein

Publication date: 19 September 2018

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.01025

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (13)

Unnamed Item ⋮ Convergence guarantee for the sparse monotone single index model ⋮ Generic error bounds for the generalized Lasso with sub-exponential data ⋮ A unified approach to uniform signal recovery from nonlinear observations ⋮ Unnamed Item ⋮ Misspecified nonconvex statistical optimization for sparse phase retrieval ⋮ Robust inference for high‐dimensional single index models ⋮ Robust high-dimensional tuning free multiple testing ⋮ Understanding Implicit Regularization in Over-Parameterized Single Index Model ⋮ Isotonic regression meets Lasso ⋮ Least squares estimation in the monotone single index model ⋮ Robust parameter estimation of regression models under weakened moment assumptions ⋮ Optimal combination of linear and spectral estimators for generalized linear models

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