  Sampling from various kinds of distributions is an issue of paramount
importance in statistics since it is often the key ingredient for constructing
estimators, test procedures or confidence intervals. In many situations, the
exact sampling from a given distribution is impossible or computationally
expensive and, therefore, one needs to resort to approximate sampling
strategies. However, it is only very recently that a mathematical theory
providing non-asymptotic guarantees for approximate sampling problem in the
high-dimensional settings started to be developed. In this paper we introduce a
new mathematical framework that helps to analyze the Stochastic Gradient
Descent as a method of sampling, closely related to Langevin Monte-Carlo.
