We study Q-learning with Polyak-Ruppert averaging (aka, averaged Q-learning) in a
discounted markov decision process in synchronous and tabular settings. Under a Lipschitz …

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

Conventional wisdom in machine learning holds that the more data you train your model on,
the better the model can perform. Accordingly, a plethora of federated learning methods …

In this paper we propose a general framework to perform statistical online inference in a
class of constant step size stochastic approximation (SA) problems, including the well …

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 …

In this paper, we develop a statistical inference procedure using stochastic gradient descent
(SGD)-based confidence intervals. These intervals are of the simplest possible form …

We introduce a new class of algorithms, stochastic generalized method of moments
(SGMM), for estimation and inference on (overidentified) moment restriction models. Our …

Big data analytics has opened new avenues in economic research, but the challenge of
analyzing datasets with tens of millions of observations is substantial. Conventional …

This paper presents a selective review of statistical computation methods for massive data
analysis. A huge amount of statistical methods for massive data computation have been …

Uncertainty quantification for estimation through stochastic optimization solutions in an
online setting has gained popularity recently. This paper introduces a novel inference …