Saved in:
| Main Authors: | Lauand, Caio Kalil, Meyn, Sean |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04424 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging
by: Lauand, Caio Kalil, et al.
Published: (2022)
by: Lauand, Caio Kalil, et al.
Published: (2022)
Markovian Foundations for Quasi-Stochastic Approximation with Applications to Extremum Seeking Control
by: Lauand, Caio Kalil, et al.
Published: (2022)
by: Lauand, Caio Kalil, et al.
Published: (2022)
Revisiting Step-Size Assumptions in Stochastic Approximation
by: Lauand, Caio Kalil, et al.
Published: (2024)
by: Lauand, Caio Kalil, et al.
Published: (2024)
The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
by: Lauand, Caio Kalil, et al.
Published: (2023)
by: Lauand, Caio Kalil, et al.
Published: (2023)
Optimistic Training and Convergence of Q-Learning -- Extended Version
by: Mehta, Prashant, et al.
Published: (2026)
by: Mehta, Prashant, et al.
Published: (2026)
Markovian Foundations for Quasi-Stochastic Approximation in Two Timescales: Extended Version
by: Lauand, Caio Kalil, et al.
Published: (2024)
by: Lauand, Caio Kalil, et al.
Published: (2024)
Stability and Sensitivity Analysis of Relative Temporal-Difference Learning: Extended Version
by: Sakha, Masoud S., et al.
Published: (2026)
by: Sakha, Masoud S., et al.
Published: (2026)
A Short Survey of Averaging Techniques in Stochastic Gradient Methods
by: Lakshmanan, K.
Published: (2026)
by: Lakshmanan, K.
Published: (2026)
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
by: Sheshukova, Marina, et al.
Published: (2024)
by: Sheshukova, Marina, et al.
Published: (2024)
An Iterative Bayesian Robbins--Monro Sequence
by: Liu, Siwei, et al.
Published: (2025)
by: Liu, Siwei, et al.
Published: (2025)
Stochastic Online Feedback Optimization for Networks of Non-Compliant Agents
by: Lauand, Caio Kalil, et al.
Published: (2025)
by: Lauand, Caio Kalil, et al.
Published: (2025)
A dynamic view of some anomalous phenomena in SGD
by: Borkar, Vivek Shripad
Published: (2025)
by: Borkar, Vivek Shripad
Published: (2025)
Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming
by: Wu, Hao
Published: (2026)
by: Wu, Hao
Published: (2026)
Optimal control under unknown intensity with Bayesian learning
by: Baradel, Nicolas, et al.
Published: (2024)
by: Baradel, Nicolas, et al.
Published: (2024)
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
by: Samsonov, Sergey, et al.
Published: (2023)
by: Samsonov, Sergey, et al.
Published: (2023)
Sample Complexity of Policy Gradient for Log-Growth Control
by: Pan, Qiuhua, et al.
Published: (2026)
by: Pan, Qiuhua, et al.
Published: (2026)
Unbiased Gradients for a Class of Conditional Stochastic Optimization Problems
by: Alvarez, Miguel, et al.
Published: (2026)
by: Alvarez, Miguel, et al.
Published: (2026)
High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation
by: Levin, Ilya, et al.
Published: (2025)
by: Levin, Ilya, et al.
Published: (2025)
Revisiting Stochastic Approximation and Stochastic Gradient Descent
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)
Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)
by: Samsonov, Sergey, et al.
Published: (2025)
Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective
by: Zantedeschi, Daniel, et al.
Published: (2026)
by: Zantedeschi, Daniel, et al.
Published: (2026)
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
by: Parletta, Daniela A., et al.
Published: (2022)
by: Parletta, Daniela A., et al.
Published: (2022)
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)
by: Butyrin, Bogdan, 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)
by: Samsonov, Sergey, et al.
Published: (2024)
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications
by: Karandikar, Rajeeva L., et al.
Published: (2023)
by: Karandikar, Rajeeva L., 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)
by: Dixon, Thomas O., et al.
Published: (2025)
Average-reward reinforcement learning in semi-Markov decision processes via relative value iteration
by: Yu, Huizhen, et al.
Published: (2025)
by: Yu, Huizhen, et al.
Published: (2025)
Asynchronous Stochastic Approximation with Applications to Average-Reward Reinforcement Learning
by: Yu, Huizhen, et al.
Published: (2024)
by: Yu, Huizhen, et al.
Published: (2024)
A Sequential Testing Problem with Signal Control
by: Campbell, Steven, et al.
Published: (2025)
by: Campbell, Steven, et al.
Published: (2025)
Multi-Iteration Stochastic Optimizers
by: Carlon, Andre, et al.
Published: (2020)
by: Carlon, Andre, et al.
Published: (2020)
When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold
by: An, Sunyoung, et al.
Published: (2026)
by: An, Sunyoung, et al.
Published: (2026)
A weak convergence approach to large deviations for stochastic approximations
by: Hult, Henrik, et al.
Published: (2025)
by: Hult, Henrik, et al.
Published: (2025)
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
by: Sheshukova, Marina, et al.
Published: (2025)
by: Sheshukova, Marina, et al.
Published: (2025)
A geometric ensemble method for Bayesian inference
by: Popov, Andrey A
Published: (2025)
by: Popov, Andrey A
Published: (2025)
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
by: Gess, Benjamin, et al.
Published: (2024)
by: Gess, Benjamin, et al.
Published: (2024)
A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence
by: Liu, Siwei, et al.
Published: (2024)
by: Liu, Siwei, et al.
Published: (2024)
On the Rate of Gaussian Approximation for Linear Regression Problems
by: Khusainov, Marat, et al.
Published: (2025)
by: Khusainov, Marat, et al.
Published: (2025)
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)
by: Butyrin, Bogdan, et al.
Published: (2025)
Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
by: Yang, Peiqi, et al.
Published: (2025)
by: Yang, Peiqi, et al.
Published: (2025)
ASMOP: Additional sampling stochastic trust region method for multi-objective problems
by: Jerinkić, Nataša Krklec, et al.
Published: (2025)
by: Jerinkić, Nataša Krklec, et al.
Published: (2025)
Similar Items
-
Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging
by: Lauand, Caio Kalil, et al.
Published: (2022) -
Markovian Foundations for Quasi-Stochastic Approximation with Applications to Extremum Seeking Control
by: Lauand, Caio Kalil, et al.
Published: (2022) -
Revisiting Step-Size Assumptions in Stochastic Approximation
by: Lauand, Caio Kalil, et al.
Published: (2024) -
The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
by: Lauand, Caio Kalil, et al.
Published: (2023) -
Optimistic Training and Convergence of Q-Learning -- Extended Version
by: Mehta, Prashant, et al.
Published: (2026)