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Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic
  Rates of Martingale CLT

Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT

3 April 2019
Andreas Anastasiou
Krishnakumar Balasubramanian
Murat A. Erdogdu
ArXiv (abs)PDFHTML

Papers citing "Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT"

16 / 16 papers shown
Title
Statistical Inference for Online Algorithms
Statistical Inference for Online Algorithms
Selina Carter
Arun K Kuchibhotla
60
1
0
22 May 2025
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
S. Samsonov
Eric Moulines
Qi-Man Shao
Zhuo-Song Zhang
Alexey Naumov
97
5
0
26 May 2024
Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
R. Srikant
115
6
0
28 Jan 2024
High Confidence Level Inference is Almost Free using Parallel Stochastic
  Optimization
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
Wanrong Zhu
Zhipeng Lou
Ziyang Wei
Wei Biao Wu
85
3
0
17 Jan 2024
Multiple Instance Learning for Uplift Modeling
Multiple Instance Learning for Uplift Modeling
Yao Zhao
Haipeng Zhang
Shiwei Lyu
Ruiying Jiang
Jinjie Gu
Guannan Zhang
72
2
0
15 Dec 2023
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Ziyang Wei
Wanrong Zhu
Wei Biao Wu
130
5
0
13 Jul 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Bhavya Agrawalla
Krishnakumar Balasubramanian
Promit Ghosal
109
2
0
20 Feb 2023
High-dimensional limit theorems for SGD: Effective dynamics and critical
  scaling
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
Gerard Ben Arous
Reza Gheissari
Aukosh Jagannath
137
59
0
08 Jun 2022
Bounds in $L^1$ Wasserstein distance on the normal approximation of
  general M-estimators
Bounds in L1L^1L1 Wasserstein distance on the normal approximation of general M-estimators
François Bachoc
M. Fathi
41
0
0
18 Nov 2021
Fast and Robust Online Inference with Stochastic Gradient Descent via
  Random Scaling
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
S. Lee
Yuan Liao
M. Seo
Youngki Shin
92
32
0
06 Jun 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise
  Variance
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
Hongjian Wang
Mert Gurbuzbalaban
Lingjiong Zhu
Umut cSimcsekli
Murat A. Erdogdu
83
42
0
20 Feb 2021
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order
  Stochastic Gradient Algorithm
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm
Yanhao Jin
Tesi Xiao
Krishnakumar Balasubramanian
72
6
0
10 Feb 2021
Berry--Esseen Bounds for Multivariate Nonlinear Statistics with
  Applications to M-estimators and Stochastic Gradient Descent Algorithms
Berry--Esseen Bounds for Multivariate Nonlinear Statistics with Applications to M-estimators and Stochastic Gradient Descent Algorithms
Q. Shao
Zhuohui Zhang
78
24
0
09 Feb 2021
Stochastic Multi-level Composition Optimization Algorithms with
  Level-Independent Convergence Rates
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates
Krishnakumar Balasubramanian
Saeed Ghadimi
A. Nguyen
127
34
0
24 Aug 2020
An Analysis of Constant Step Size SGD in the Non-convex Regime:
  Asymptotic Normality and Bias
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu
Krishnakumar Balasubramanian
S. Volgushev
Murat A. Erdogdu
106
52
0
14 Jun 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and
  Non-Asymptotic Concentration
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
Wenlong Mou
C. J. Li
Martin J. Wainwright
Peter L. Bartlett
Michael I. Jordan
85
76
0
09 Apr 2020
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