Explore the Randomized Midpoint Langevin Monte Carlo (RMP-LCM) method in this 39-minute lecture by Arnak Dalalyan from ENSAE Paris, France. Delve into the efficiency and widespread use of Langevin Monte Carlo for generating random samples from target distributions in high-dimensional Euclidean spaces. Examine various Langevin Monte Carlo variants and understand why RMP-LCM offers the best known non-asymptotic theoretical guarantees on sampling error for target distributions with continuous Lipschitz gradient log-densities. Review existing results, discover extensions, and learn about recent improvements to this powerful sampling technique.