Regularized Optimal Transport is Ground Cost Adversarial

ICML 2020 François-Pierre PatyMarco Cuturi

Regularizing the optimal transport (OT) problem has proven crucial for OT theory to impact the field of machine learning. For instance, it is known that regularizing OT problems with entropy leads to faster computations and better differentiation using the Sinkhorn algorithm, as well as better sample complexity bounds than classic OT... (read more)

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