Optimal Transport for Machine Learners
It serves as an educational resource for machine learners by explaining OT theory and its relevance to the field, but it is incremental as it compiles existing knowledge rather than introducing new research.
The paper provides course notes on Optimal Transport (OT), covering its mathematical foundations and numerical methods, and discusses its applications in machine learning, such as in generative models and neural network training, without presenting new experimental results or concrete numbers.
Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributions and has recently become an important tool in machine learning, especially for designing and evaluating generative models. These course notes cover the fundamental mathematical aspects of OT, including the Monge and Kantorovich formulations, Brenier's theorem, the dual and dynamic formulations, the Bures metric on Gaussian distributions, and gradient flows. It also introduces numerical methods such as linear programming, semi-discrete solvers, and entropic regularization. Applications in machine learning include topics like training neural networks via gradient flows, token dynamics in transformers, and the structure of GANs and diffusion models. These notes focus primarily on mathematical content rather than deep learning techniques.