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Statistical consistency and asymptotic normality for high-dimensional
  robust M-estimators

Statistical consistency and asymptotic normality for high-dimensional robust M-estimators

1 January 2015
Po-Ling Loh
ArXivPDFHTML

Papers citing "Statistical consistency and asymptotic normality for high-dimensional robust M-estimators"

9 / 9 papers shown
Title
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
Yixuan Zhang
Dongyan
Yudong Chen
Qiaomin Xie
19
0
0
11 Apr 2025
Statistical Guarantees for Approximate Stationary Points of Simple
  Neural Networks
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks
Mahsa Taheri
Fang Xie
Johannes Lederer
16
0
0
09 May 2022
High-dimensional nonconvex lasso-type $M$-estimators
High-dimensional nonconvex lasso-type MMM-estimators
Jad Beyhum
Franccois Portier
11
0
0
12 Apr 2022
A Bernstein-type Inequality for High Dimensional Linear Processes with
  Applications to Robust Estimation of Time Series Regressions
A Bernstein-type Inequality for High Dimensional Linear Processes with Applications to Robust Estimation of Time Series Regressions
Linbo Liu
Danna Zhang
AI4TS
16
0
0
21 Sep 2021
Differentially private inference via noisy optimization
Differentially private inference via noisy optimization
Marco Avella-Medina
Casey Bradshaw
Po-Ling Loh
FedML
14
29
0
19 Mar 2021
Approximate Laplace approximations for scalable model selection
Approximate Laplace approximations for scalable model selection
D. Rossell
Oriol Abril
A. Bhattacharya
8
15
0
14 Dec 2020
Robust Estimation via Robust Gradient Estimation
Robust Estimation via Robust Gradient Estimation
Adarsh Prasad
A. Suggala
Sivaraman Balakrishnan
Pradeep Ravikumar
20
219
0
19 Feb 2018
Complete Dictionary Recovery over the Sphere
Complete Dictionary Recovery over the Sphere
Ju Sun
Qing Qu
John N. Wright
28
202
0
26 Apr 2015
Learning without Concentration for General Loss Functions
Learning without Concentration for General Loss Functions
S. Mendelson
55
65
0
13 Oct 2014
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