Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms.

In this work, we build on this framework and proposeAvare, a simple and efficient algorithm for adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes.

Estimation and inference in statistics pose significant challenges when data are collected adaptively.