A single spectral sandwich on the Sinkhorn linearization yields identifiability, sparsistency, well-posedness, and convergence bounds for feature-parameterized inverse optimal transport.
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Sinkhorn Linearization and the Spectral Proxy: Unifying the Statistical and Algorithmic Theory of Feature-Parameterized Inverse Optimal Transport via a Single Spectral Sandwich
A single spectral sandwich on the Sinkhorn linearization yields identifiability, sparsistency, well-posedness, and convergence bounds for feature-parameterized inverse optimal transport.