We study statistical inference and distributionally robust solution methods for stochastic
optimization problems, focusing on confidence intervals for optimal values and solutions that …

We undertake a precise study of the asymptotic and non-asymptotic properties of stochastic
approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar …

Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients; rather than sharing the data …

We analyze the novel Local SGD in federated Learning, a multi-round estimation procedure
that uses intermittent communication to improve communication efficiency. Under a $2 …

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

The recent emergence of reinforcement learning (RL) has created a demand for robust
statistical inference methods for the parameter estimates computed using these algorithms …

Pile design is an essential component of geotechnical engineering practice, and pipe piles,
in particular, are increasingly being used for the support of a variety of infrastructure projects …

We prove quantitative convergence rates at which discrete Langevin-like processes
converge to the invariant distribution of a related stochastic differential equation. We study …

In this work, we investigate stochastic approximation (SA) with Markovian data and
nonlinear updates under constant stepsize $\alpha> 0$. Existing work has primarily focused …

Stochastic gradient descent (SGD) is an immensely popular approach for online learning in
settings where data arrives in a stream or data sizes are very large. However, despite an …