arXiv Machine Learning
Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 15
Steady-State Convergence of Stochastic Approximation
arXiv:2609.14922v1 Announce Type: cross Abstract: For constant-stepsize stochastic approximation (SA), the iterates converge in distribution to a stationary law that depends on the stepsize $\alpha.$...
arXiv Machine Learning
Sep 10
Constant-Stepsize Stochastic Approximation: Finite-Time Convergence, Gaussian Approximation, and Tail Bounds
arXiv:2602.13960v2 Announce Type: replace Abstract: Constant-stepsize stochastic approximation (SA) is widely used in learning for computational efficiency, yet the distribution of the iterates is ty...
arXiv Statistics ML
Aug 25
Q-Learning with Stable Infinite-Dimensional Linear Function Approximation
arXiv:2608.22636v1 Announce Type: cross Abstract: Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture need not preserve the Bellman contracti...
arXiv Machine Learning
Oct 2
Sharp Stationary Gaussian Approximation for Constant-Stepsize SGD
arXiv:2609. 39144v2 Announce Type: new Abstract: We prove a sharp Gaussian approximation for the invariant law of constant-stepsize SGD with bounded additive noise generated by an exogenous uniformly ergodic Markov chain.
arXiv Machine Learning
Oct 16
Stochastic Gradient Descent: A Nonlinear Time Series Perspective
This paper studies the statistical properties of Stochastic Gradient Descent (SGD) with a constant step size using the framework of iterated random functions. It demonstrates convergence in Euclidean distance via Geometric Moment Contraction, establishes quenched central limit theorems, invariance principles, and α-stable convergence for averaged SGD regardless of initialization, and explores the effect of step sizes through derivative processes. The authors further develop a Richardson-Romberg extrapolation with multiple step sizes, propose a bias-reduced variant, and introduce an online inference method that yields empirical confidence intervals with coverage approaching nominal levels as sample size increases.
arXiv AI
Jun 19
Robust $Q$-learning for mean-field control under Wasserstein uncertainty in common noise
arXiv:2606. 20356v1 Announce Type: cross Abstract: In this article, we present a robust $Q$-learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law.