For min-max optimization and variational inequalities problems (VIPs), Stochastic
Extragradient (SEG) and Stochastic Gradient Descent Ascent (SGDA) have emerged as …

In this paper we consider the problem of obtaining sharp bounds for the performance of
temporal difference (TD) methods with linear function approximation for policy evaluation in …

In this paper we consider the problem of obtaining sharp bounds for the performance of
temporal difference (TD) methods with linear functional approximation for policy evaluation …

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 prove a non-asymptotic central limit theorem for vector-valued martingale differences
using Stein's method, and use Poisson's equation to extend the result to functions of Markov …

Theory and application of stochastic approximation (SA) has grown within the control
systems community since the earliest days of adaptive control. This paper takes a new look …

We study stochastic approximation algorithms with Markovian noise and constant step-size
$\alpha $. We develop a method based on infinitesimal generator comparisons to study the …

In this paper, we establish novel deviation bounds for additive functionals of geometrically
ergodic Markov chains similar to Rosenthal and Bernstein inequalities for sums of …

This paper proposes an asymptotic theory for online inference of the stochastic gradient
descent (SGD) iterates with dropout regularization in linear regression. Specifically, we …

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 …