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Tight High Probability Bounds for Linear Stochastic Approximation with
  Fixed Stepsize

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

2 June 2021
Alain Durmus
Eric Moulines
A. Naumov
S. Samsonov
Kevin Scaman
Hoi-To Wai
ArXivPDFHTML

Papers citing "Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize"

5 / 5 papers shown
Title
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
33
4
0
26 May 2024
High-dimensional scaling limits and fluctuations of online least-squares
  SGD with smooth covariance
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
Krishnakumar Balasubramanian
Promit Ghosal
Ye He
38
5
0
03 Apr 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
25
2
0
20 Feb 2023
Learning from time-dependent streaming data with online stochastic
  algorithms
Learning from time-dependent streaming data with online stochastic algorithms
Antoine Godichon-Baggioni
Nicklas Werge
Olivier Wintenberger
35
3
0
25 May 2022
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis
  and Application to Actor-Critic
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic
Mingyi Hong
Hoi-To Wai
Zhaoran Wang
Zhuoran Yang
18
135
0
10 Jul 2020
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