Revisiting the Constant Stepsize Stochastic Approximation with Decision-Dependent Markovian Noise
Fuente:
arXiv
Saved in:
| Main Authors: | Hadavi, Hadi, Mou, Wenlong, Samsonov, Sergey, Wai, Hoi-To |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
by: Huo, Dongyan, et al.
Published: (2022)
by: Huo, Dongyan, et al.
Published: (2022)
Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)
by: Samsonov, Sergey, et al.
Published: (2025)
Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
by: Allmeier, Sebastian, et al.
Published: (2024)
by: Allmeier, Sebastian, et al.
Published: (2024)
The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
by: Huo, Dongyan, et al.
Published: (2024)
by: Huo, Dongyan, et al.
Published: (2024)
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
Stochastic Approximation with Block Coordinate Optimal Stepsizes
by: Jiang, Tao, et al.
Published: (2025)
by: Jiang, Tao, et al.
Published: (2025)
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
by: Levin, Ilya, et al.
Published: (2026)
by: Levin, Ilya, et al.
Published: (2026)
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
by: Liu, Haitong, et al.
Published: (2023)
by: Liu, Haitong, et al.
Published: (2023)
Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss
by: Li, Qiang, et al.
Published: (2024)
by: Li, Qiang, et al.
Published: (2024)
Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity
by: Li, Qiang, et al.
Published: (2024)
by: Li, Qiang, et al.
Published: (2024)
Decentralized Learning with Dynamically Refined Edge Weights: A Data-Dependent Framework
by: Du, Rongxing, et al.
Published: (2026)
by: Du, Rongxing, et al.
Published: (2026)
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, et al.
Published: (2024)
by: Haque, Shaan Ul, et al.
Published: (2024)
Generalization of Silver Stepsize Schedule to Stochastic Optimization
by: Bai, Luwei, et al.
Published: (2025)
by: Bai, Luwei, et al.
Published: (2025)
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
by: Agrawal, Shubhada, et al.
Published: (2026)
by: Agrawal, Shubhada, et al.
Published: (2026)
A Strengthened Conjecture on the Minimax Optimal Constant Stepsize for Gradient Descent
by: Grimmer, Benjamin, et al.
Published: (2024)
by: Grimmer, Benjamin, et al.
Published: (2024)
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
by: Haque, Shaan Ul, et al.
Published: (2023)
by: Haque, Shaan Ul, et al.
Published: (2023)
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)
by: Qian, Xiaochi, et al.
Published: (2024)
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by: Hu, Jie, et al.
Published: (2024)
by: Hu, Jie, et al.
Published: (2024)
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)
by: Mangold, Paul, et al.
Published: (2024)
Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)
by: Jiang, Nanfei, et al.
Published: (2025)
Asynchronous and Stochastic Distributed Resource Allocation
by: Li, Qiang, et al.
Published: (2025)
by: Li, Qiang, et al.
Published: (2025)
Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
by: Yau, Chung-Yiu, et al.
Published: (2024)
by: Yau, Chung-Yiu, et al.
Published: (2024)
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
by: Chandak, Siddharth, et al.
Published: (2025)
by: Chandak, Siddharth, et al.
Published: (2025)
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation
by: Zhang, Yixuan, et al.
Published: (2024)
by: Zhang, Yixuan, et al.
Published: (2024)
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)
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)
Acceleration by Stepsize Hedging II: Silver Stepsize Schedule for Smooth Convex Optimization
by: Altschuler, Jason M., et al.
Published: (2023)
by: Altschuler, Jason M., et al.
Published: (2023)
Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)
by: Chandak, Siddharth, et al.
Published: (2026)
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)
by: Butyrin, Bogdan, et al.
Published: (2025)
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
by: Orvieto, Antonio, et al.
Published: (2022)
by: Orvieto, Antonio, et al.
Published: (2022)
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
by: Rodomanov, Anton, et al.
Published: (2024)
by: Rodomanov, Anton, et al.
Published: (2024)
Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
by: Mou, Wenlong
Published: (2026)
by: Mou, Wenlong
Published: (2026)
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)
by: Mou, Wenlong, et al.
Published: (2021)
Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent
by: Li, Xiang, et al.
Published: (2024)
by: Li, Xiang, et al.
Published: (2024)
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
by: Orvieto, Antonio, et al.
Published: (2024)
by: Orvieto, Antonio, et al.
Published: (2024)
Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs
by: Mou, Wenlong
Published: (2025)
by: Mou, Wenlong
Published: (2025)
Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models
by: Mou, Wenlong
Published: (2025)
by: Mou, Wenlong
Published: (2025)
The L-Shaped Method for Stochastic Programs with Decision-Dependent Uncertainty
by: Pantuso, Giovanni, et al.
Published: (2025)
by: Pantuso, Giovanni, et al.
Published: (2025)
Similar Items
-
Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
by: Huo, Dongyan, et al.
Published: (2022) -
Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025) -
Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
by: Allmeier, Sebastian, et al.
Published: (2024) -
The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
by: Huo, Dongyan, et al.
Published: (2024) -
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)