A Distributed Framework for the Construction of Transport Maps

Understand

The need to reason about uncertainty in large, complex, and multi-modal datasets has become increasingly common across modern scientific environments.

• The ability to transform samples from one distribution $P$ to another distribution $Q$ enables the solution to many problems in machine learning (e.g.
• Bayesian inference, generative modeling) and has been actively pursued from theoretical, computational, and application perspectives across the fields of information theory, computer science, and biology.
• Performing such transformations, in general, still leads to computational difficulties, especially in high dimensions.