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Distributed Statistical Estimation and Rates of Convergence in Normal
  Approximation

Distributed Statistical Estimation and Rates of Convergence in Normal Approximation

9 April 2017
Stanislav Minsker
Nate Strawn
ArXivPDFHTML

Papers citing "Distributed Statistical Estimation and Rates of Convergence in Normal Approximation"

16 / 16 papers shown
Title
Byzantine-tolerant distributed learning of finite mixture models
Byzantine-tolerant distributed learning of finite mixture models
Qiong Zhang
Jiahua Chen
Jiahua Chen
FedML
37
0
0
19 Jul 2024
Copula Approximate Bayesian Computation Using Distribution Random
  Forests
Copula Approximate Bayesian Computation Using Distribution Random Forests
G. Karabatsos
32
1
0
28 Feb 2024
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian,
  and Beyond $1+α$ Moments
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond 1+α1+α1+α Moments
Trung Dang
Jasper C. H. Lee
Maoyuan Song
Paul Valiant
11
1
0
21 Nov 2023
Machine Learning and the Future of Bayesian Computation
Machine Learning and the Future of Bayesian Computation
Steven Winter
Trevor Campbell
Lizhen Lin
Sanvesh Srivastava
David B. Dunson
TPM
24
4
0
21 Apr 2023
A review of distributed statistical inference
A review of distributed statistical inference
Yuan Gao
Weidong Liu
Hansheng Wang
Xiaozhou Wang
Yibo Yan
Riquan Zhang
8
42
0
13 Apr 2023
On deviation probabilities in non-parametric regression
On deviation probabilities in non-parametric regression
Anna Ben-Hamou
A. Guyader
20
1
0
25 Jan 2023
Decentralized Federated Learning: Fundamentals, State of the Art,
  Frameworks, Trends, and Challenges
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
Enrique Tomás Martínez Beltrán
Mario Quiles Pérez
Pedro Miguel Sánchez Sánchez
Sergio López Bernal
Gérome Bovet
M. Pérez
Gregorio Martínez Pérez
Alberto Huertas Celdrán
FedML
18
221
0
15 Nov 2022
U-statistics of growing order and sub-Gaussian mean estimators with
  sharp constants
U-statistics of growing order and sub-Gaussian mean estimators with sharp constants
Stanislav Minsker
16
13
0
24 Feb 2022
Do we need to estimate the variance in robust mean estimation?
Do we need to estimate the variance in robust mean estimation?
Qiang Sun
OOD
18
7
0
30 Jun 2021
Robust subgaussian estimation with VC-dimension
Robust subgaussian estimation with VC-dimension
Jules Depersin
25
12
0
24 Apr 2020
Robust subgaussian estimation of a mean vector in nearly linear time
Robust subgaussian estimation of a mean vector in nearly linear time
Jules Depersin
Guillaume Lecué
21
92
0
07 Jun 2019
Second Order Expansions for Sample Median with Random Sample Size
Second Order Expansions for Sample Median with Random Sample Size
G. Christoph
V. Ulyanov
V. Bening
15
5
0
19 May 2019
Robust high dimensional learning for Lipschitz and convex losses
Robust high dimensional learning for Lipschitz and convex losses
Geoffrey Chinot
Guillaume Lecué
M. Lerasle
15
18
0
10 May 2019
Uniform bounds for robust mean estimators
Uniform bounds for robust mean estimators
Stanislav Minsker
OOD
FedML
10
35
0
09 Dec 2018
Defending Against Saddle Point Attack in Byzantine-Robust Distributed
  Learning
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin
Yudong Chen
K. Ramchandran
Peter L. Bartlett
FedML
18
97
0
14 Jun 2018
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
M. Lerasle
Z. Szabó
Gaspar Massiot
Guillaume Lecué
20
33
0
13 Feb 2018
1