Structured Optimal Transport

Understand

Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning.

• Yet, its ability to capture frequently occurring structure beyond the "ground metric" is limited.
• In this work, we develop a nonlinear generalization of (discrete) optimal transport that is able to reflect much additional structure.
• We demonstrate how to leverage the geometry of this new model for fast algorithms, and explore connections and properties.