A deep learning penalty method using maximum mean discrepancy enforces marginal constraints in multi-marginal Monge transport, with a convergence theorem for constraint satisfaction only.
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A deep learning approach to multi-marginal optimal transport via Hilbert space embeddings of probability measures
A deep learning penalty method using maximum mean discrepancy enforces marginal constraints in multi-marginal Monge transport, with a convergence theorem for constraint satisfaction only.