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Scaled minimax optimality in high-dimensional linear regression: A
  non-convex algorithmic regularization approach

Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach

27 August 2020
M. Ndaoud
ArXiv (abs)PDFHTML

Papers citing "Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach"

7 / 7 papers shown
Title
Adaptive signal recovery in sparse nonparametric models
Adaptive signal recovery in sparse nonparametric models
Natalia Stepanova
Marie Turcicova
53
0
0
06 Nov 2024
Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model
Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model
V. Karagulyan
M. Ndaoud
81
0
0
18 Jun 2024
A minimax optimal approach to high-dimensional double sparse linear
  regression
A minimax optimal approach to high-dimensional double sparse linear regression
Yanhang Zhang
Zhifan Li
J. Yin
55
6
0
07 May 2023
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope
Stanislav Minsker
M. Ndaoud
Lan Wang
108
8
0
30 Oct 2022
Minimax Rates for High-dimensional Double Sparse Structure over
  $\ell_u(\ell_q)$-balls
Minimax Rates for High-dimensional Double Sparse Structure over ℓu(ℓq)\ell_u(\ell_q)ℓu​(ℓq​)-balls
Zhifan Li
Yanhang Zhang
J. Yin
24
0
0
25 Jul 2022
Variable selection, monotone likelihood ratio and group sparsity
Variable selection, monotone likelihood ratio and group sparsity
C. Butucea
E. Mammen
M. Ndaoud
Alexandre B. Tsybakov
101
4
0
30 Dec 2021
De-biasing convex regularized estimators and interval estimation in
  linear models
De-biasing convex regularized estimators and interval estimation in linear models
Pierre C. Bellec
Cun-Hui Zhang
128
20
0
26 Dec 2019
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