Seminari del Dipartimento di Matematica

Seminario del 2020

Scaling Optimal Transport for High dimensional Learning

nel ciclo di seminari: MATHEMATICAL METHODS AND MODELS IN MACHINE LEARNING

fisica matematica

Optimal transport (OT) has recently gained lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will review entropic regularization methods which define geometric loss functions approximating OT with a better sample complexity. More information and references can be found on the website of our book "Computational Optimal Transport " https://optimaltransport.github.io/