volesti: sampling efficiently from high dimensional distributions

Sampling from multidimensional distributions is a fundamental operation that plays a crucial role across sciences including modern machine learning and data science. An impressive number of important problems such as optimization and integration can be efficiently solved via sampling.

This talk is an introductory tutorial on open-source software volesti, a C++ package with R and Python interfaces. volesti offers efficient implementations of state-of-the-art algorithms for sampling as well as volume computation of convex sets in high dimensions. volesti provides the most efficient implementations for sampling and volume to date allowing users to solve problems that cannot be solved with alternative software packages.

The structure of the talk has two parts: first an introduction to volesti library and relevant background and second a tutorial that shows how volesti can be used with a focus on applications in artificial intelligence, finance and bioinformatics.