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Fundamental Barriers to High-Dimensional Regression with Convex
  Penalties

Fundamental Barriers to High-Dimensional Regression with Convex Penalties

25 March 2019
Michael Celentano
Andrea Montanari
ArXivPDFHTML

Papers citing "Fundamental Barriers to High-Dimensional Regression with Convex Penalties"

6 / 6 papers shown
Title
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning
Pierre C. Bellec
Yi Shen
27
13
0
03 Jan 2025
Approximate message passing from random initialization with applications
  to $\mathbb{Z}_{2}$ synchronization
Approximate message passing from random initialization with applications to Z2\mathbb{Z}_{2}Z2​ synchronization
Gen Li
Wei Fan
Yuting Wei
21
10
0
07 Feb 2023
Performance of Bayesian linear regression in a model with mismatch
Performance of Bayesian linear regression in a model with mismatch
Jean Barbier
Wei-Kuo Chen
D. Panchenko
Manuel Sáenz
32
22
0
14 Jul 2021
Label-Imbalanced and Group-Sensitive Classification under
  Overparameterization
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Ganesh Ramachandra Kini
Orestis Paraskevas
Samet Oymak
Christos Thrampoulidis
17
93
0
02 Mar 2021
Precise Statistical Analysis of Classification Accuracies for
  Adversarial Training
Precise Statistical Analysis of Classification Accuracies for Adversarial Training
Adel Javanmard
Mahdi Soltanolkotabi
AAML
13
61
0
21 Oct 2020
SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax
SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax
Weijie Su
Emmanuel Candes
60
145
0
29 Mar 2015
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