We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a
single trajectory of Markovian data. Our methodology has practical applications in various …

We study time-uniform statistical inference for parameters in stochastic approximation (SA),
which encompasses a bunch of applications in optimization and machine learning. To that …

Stochastic approximation Markov Chain Monte Carlo (SAMCMC) algorithms are a class of
online algorithms having wide-ranging applications, particularly within Markovian systems …

In this paper, we obtain the Berry-Esseen bound for multivariate normal approximation for
the Polyak-Ruppert averaged iterates of the linear stochastic approximation (LSA) algorithm …

In two-time-scale stochastic approximation (SA), two iterates are updated at different rates,
governed by distinct step sizes, with each update influencing the other. Previous studies …

This article investigates the problem of online statistical inference of model parameters in
stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search …

Large-scale data characterized by heterogeneity due to heteroscedastic variance or
inhomogeneous covariate effects arises in diverse fields of scientific research and …