Cover Image

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Agrawal, Shubhada, Maguluri, Siva Theja, Zubeldia, Martin
Format:	Preprint
Published:	2026
Subjects:	Probability Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
by: Chen, Zaiwei, et al.
Published: (2023)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, 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)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
by: Agrawal, Shubhada, et al.
Published: (2024)

Almost Sure Convergence of Stochastic Approximation: An Interplay of Noise and Step Size
by: Nguyen, Quang Dinh Thien, et al.
Published: (2026)

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
by: Dang, Thanh, et al.
Published: (2025)

Dynamic Pricing and Matching for Two-Sided Queues
by: Varma, Sushil Mahavir, et al.
Published: (2019)

Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework
by: Chen, Zaiwei, et al.
Published: (2026)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
by: Wang, Zedong, et al.
Published: (2026)

A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms
by: Chen, Zaiwei, et al.
Published: (2025)

Performance of NPG in Countable State-Space Average-Cost RL
by: Murthy, Yashaswini, et al.
Published: (2024)

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
by: Zhu, Tianxi, et al.
Published: (2025)

Tail Bounds for Queues with Abandonment: Constant, Moderate, Large Deviations, and Efficient Concentration
by: Wang, Zedong, et al.
Published: (2026)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)

Stochastic Approximation for Nonlinear Discrete Stochastic Control: Finite-Sample Bounds
by: Nguyen, Hoang Huy, et al.
Published: (2023)

Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
by: Armacki, Aleksandar, et al.
Published: (2024)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
by: Allmeier, Sebastian, et al.
Published: (2024)

Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise
by: Wang, Menglian, et al.
Published: (2026)

Optimal Asynchronous Stochastic Nonconvex Optimization under Heavy-Tailed Noise
by: Wu, Yidong, et al.
Published: (2026)

In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
by: Liu, Zijian
Published: (2026)

High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2021)

Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise
by: Yu, Dingzhi, et al.
Published: (2026)

Can SGD Handle Heavy-Tailed Noise?
by: Fatkhullin, Ilyas, et al.
Published: (2025)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
by: Huynh, Phuoc-Toan, et al.
Published: (2026)

Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by: Hu, Jie, et al.
Published: (2024)

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
by: Liu, Zijian, et al.
Published: (2024)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2023)

Approximation and interpolation of deep neural networks
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)

Flatness-Aware Stochastic Gradient Langevin Dynamics
by: Bruno, Stefano, et al.
Published: (2025)

Electric Vehicle Fleet and Charging Infrastructure Planning
by: Varma, Sushil Mahavir, et al.
Published: (2023)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
by: Li, Qin, et al.
Published: (2024)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
by: Ustimenko, Aleksei, et al.
Published: (2023)

Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
by: Cory-Wright, Ryan, et al.
Published: (2025)

Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
by: Choudhury, Sayantan, et al.
Published: (2026)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
by: Kassing, Sebastian, et al.
Published: (2025)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
by: Hu, Jie, et al.
Published: (2024)

Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise
by: Kanakeri, Vinay, et al.
Published: (2025)