Recorded 19 May 2025. Giulia Luise of Microsoft presents "Learning to optimize transport plans" at IPAM's Statistical and Numerical Methods for Non-commutative Optimal Transport Workshop. Abstract: Optimal transport distances and their regularized versions are a powerful tool to compare probability measures, that proved successful in many machine learning applications. In this talk, I will give a brief introduction on (entropy-regularized) optimal transport and dive into ’learning to optimize’ transport plans leveraging amortized optimization. Joint work with Brandon Amos (META), Samuel Cohen (UCL), Ievgen Redko (Aalto University). Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-iii-statistical-and-numerical-methods-for-non-commutative-optimal-transport/