Cover Image

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sheshukova, Marina, Belomestny, Denis, Durmus, Alain, Moulines, Eric, Naumov, Alexey, Samsonov, Sergey
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control Machine Learning 62L20
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation
by: Levin, Ilya, et al.
Published: (2025)

Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
by: Sheshukova, Marina, et al.
Published: (2025)

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
by: Samsonov, Sergey, et al.
Published: (2023)

On the Rate of Gaussian Approximation for Linear Regression Problems
by: Khusainov, Marat, et al.
Published: (2025)

Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
by: Samsonov, Sergey, et al.
Published: (2024)

Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)

Refined Analysis of Federated Averaging and Federated Richardson-Romberg
by: Mangold, Paul, et al.
Published: (2024)

Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)

A Short Survey of Averaging Techniques in Stochastic Gradient Methods
by: Lakshmanan, K.
Published: (2026)

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)

Global Convergence and Acceleration for Single Observation Gradient Free Optimization
by: Lauand, Caio Kalil, et al.
Published: (2025)

Revisiting Stochastic Approximation and Stochastic Gradient Descent
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)

Rosenthal-type inequalities for linear statistics of Markov chains
by: Durmus, Alain, et al.
Published: (2023)

UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
by: Belomestny, Denis, et al.
Published: (2021)

An Iterative Bayesian Robbins--Monro Sequence
by: Liu, Siwei, et al.
Published: (2025)

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming
by: Wu, Hao
Published: (2026)

Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective
by: Zantedeschi, Daniel, et al.
Published: (2026)

Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
by: Gess, Benjamin, et al.
Published: (2024)

A dynamic view of some anomalous phenomena in SGD
by: Borkar, Vivek Shripad
Published: (2025)

Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
by: Yang, Peiqi, et al.
Published: (2025)

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
by: Mangold, Paul, et al.
Published: (2025)

High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
by: Parletta, Daniela A., et al.
Published: (2022)

Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging
by: Lauand, Caio Kalil, et al.
Published: (2022)

Optimal control under unknown intensity with Bayesian learning
by: Baradel, Nicolas, et al.
Published: (2024)

Unbiased Gradients for a Class of Conditional Stochastic Optimization Problems
by: Alvarez, Miguel, et al.
Published: (2026)

Asynchronous Stochastic Approximation with Applications to Average-Reward Reinforcement Learning
by: Yu, Huizhen, et al.
Published: (2024)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)

Sample Complexity of Policy Gradient for Log-Growth Control
by: Pan, Qiuhua, et al.
Published: (2026)

Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach
by: Kassing, Sebastian, et al.
Published: (2026)

A note on concentration inequalities for the overlapped batch mean variance estimators for Markov chains
by: Moulines, Eric, et al.
Published: (2025)

Theoretical guarantees for neural control variates in MCMC
by: Belomestny, Denis, et al.
Published: (2023)

BISTRO -- A Bi-Fidelity Stochastic Gradient Framework using Trust-Regions for Optimization Under Uncertainty
by: Dixon, Thomas O., et al.
Published: (2025)

Multi-Iteration Stochastic Optimizers
by: Carlon, Andre, et al.
Published: (2020)

Average-reward reinforcement learning in semi-Markov decision processes via relative value iteration
by: Yu, Huizhen, et al.
Published: (2025)

Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications
by: Karandikar, Rajeeva L., et al.
Published: (2023)

Robust Nonparametric Stochastic Frontier Analysis
by: Zheng, Peng, et al.
Published: (2024)

Stability and Sensitivity Analysis of Relative Temporal-Difference Learning: Extended Version
by: Sakha, Masoud S., et al.
Published: (2026)

A Sequential Testing Problem with Signal Control
by: Campbell, Steven, et al.
Published: (2025)

When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold
by: An, Sunyoung, et al.
Published: (2026)