Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality

1 code implementation • 9 Jul 2022 • Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy

We show that under mild assumptions, the deviation between the average iterate of the algorithm and the solution is asymptotically normal, with a covariance that clearly decouples the effects of the gradient noise and the distributional shift.

Stochastic Optimization under Distributional Drift

1 code implementation • NeurIPS 2021 • Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui

We consider the problem of minimizing a convex function that is evolving according to unknown and possibly stochastic dynamics, which may depend jointly on time and on the decision variable itself.

Stochastic Optimization valid

Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees

no code implementations • NeurIPS 2021 • Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui

We consider the problem of minimizing a convex function that is evolving in time according to unknown and possibly stochastic dynamics.

Stochastic Optimization valid