Two-timescale stochastic approximation (TTSA) is among the most general frameworks for
iterative stochastic algorithms. This includes well-known stochastic optimization methods …

In this paper, we study the effectiveness of using a constant stepsize in statistical inference
via linear stochastic approximation (LSA) algorithms with Markovian data. After establishing …

We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve
constrained nonlinear optimization problems, where the objective is stochastic and the …

In this paper, we investigate the theoretical properties of stochastic gradient descent (SGD)
for statistical inference in the context of nonconvex optimization problems, which have been …

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

We study a family of distributed stochastic optimization algorithms where gradients are
sampled by a token traversing a network of agents in random-walk fashion. Typically, these …

Multi-time-scale stochastic approximation is an iterative algorithm for finding the fixed point
of a set of $ N $ coupled operators given their noisy samples. It has been observed that due …

Statistical inference with finite-sample validity for the value function of a given policy in
Markov decision processes (MDPs) is crucial for ensuring the reliability of reinforcement …

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 …

Distributed learning is essential to train machine learning algorithms across heterogeneous
agents while maintaining data privacy. We conduct an asymptotic analysis of Unified …