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2004.04719
Cited By
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
9 April 2020
Wenlong Mou
C. J. Li
Martin J. Wainwright
Peter L. Bartlett
Michael I. Jordan
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Papers citing
"On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration"
24 / 24 papers shown
Title
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
Yixuan Zhang
Dongyan
Yudong Chen
Qiaomin Xie
38
0
0
11 Apr 2025
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
Zhijie Chen
Qiaobo Li
A. Banerjee
FedML
42
0
0
11 Nov 2024
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
Marina Sheshukova
Denis Belomestny
Alain Durmus
Eric Moulines
Alexey Naumov
S. Samsonov
46
1
0
07 Oct 2024
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
33
4
0
26 May 2024
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Jie Hu
Vishwaraj Doshi
Do Young Eun
38
4
0
17 Jan 2024
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
Shaan ul Haque
S. Khodadadian
S. T. Maguluri
44
11
0
31 Dec 2023
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Ziyang Wei
Wanrong Zhu
Wei Biao Wu
37
3
0
13 Jul 2023
High-probability sample complexities for policy evaluation with linear function approximation
Gen Li
Weichen Wu
Yuejie Chi
Cong Ma
Alessandro Rinaldo
Yuting Wei
OffRL
38
7
0
30 May 2023
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Adrian Redder
Arunselvan Ramaswamy
Holger Karl
25
1
0
11 May 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Bhavya Agrawalla
Krishnakumar Balasubramanian
Promit Ghosal
35
2
0
20 Feb 2023
Efficiency Ordering of Stochastic Gradient Descent
Jie Hu
Vishwaraj Doshi
Do Young Eun
31
6
0
15 Sep 2022
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Gen Li
Yuejie Chi
Yuting Wei
Yuxin Chen
37
18
0
22 Aug 2022
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences
Han Shen
Tianyi Chen
54
15
0
21 Jun 2022
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Sen Na
Michael W. Mahoney
34
7
0
27 May 2022
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
Xiang Li
Wenhao Yang
Jiadong Liang
Zhihua Zhang
Michael I. Jordan
48
15
0
29 Dec 2021
Gradient Temporal Difference with Momentum: Stability and Convergence
Rohan Deb
S. Bhatnagar
19
5
0
22 Nov 2021
Does Momentum Help? A Sample Complexity Analysis
Swetha Ganesh
Rohan Deb
Gugan Thoppe
A. Budhiraja
26
2
0
29 Oct 2021
Optimal policy evaluation using kernel-based temporal difference methods
Yaqi Duan
Mengdi Wang
Martin J. Wainwright
OffRL
28
27
0
24 Sep 2021
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
Pratik Ramprasad
Yuantong Li
Zhuoran Yang
Zhaoran Wang
W. Sun
Guang Cheng
OffRL
50
27
0
08 Aug 2021
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise
Thinh T. Doan
18
15
0
04 Apr 2021
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
Gen Li
Changxiao Cai
Ee
Yuting Wei
Yuejie Chi
OffRL
55
75
0
12 Feb 2021
Optimal oracle inequalities for solving projected fixed-point equations
Wenlong Mou
A. Pananjady
Martin J. Wainwright
29
14
0
09 Dec 2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li
Yuting Wei
Yuejie Chi
Yuxin Chen
36
125
0
26 May 2020
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir
Tong Zhang
104
572
0
08 Dec 2012
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