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Poster
Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty · Marco Cuturi

Thu Jul 16 02:00 PM -- 02:45 PM & Fri Jul 17 03:00 AM -- 03:45 AM (PDT) @
in Poster Session 47 »

Regularizing Wasserstein distances has proved to be the key in the recent advances of optimal transport (OT) in machine learning. Most prominent is the entropic regularization of OT, which not only allows for fast computations and differentiation using Sinkhorn algorithm, but also improves stability with respect to data and accuracy in many numerical experiments. Theoretical understanding of these benefits remains unclear, although recent statistical works have shown that entropy-regularized OT mitigates classical OT's curse of dimensionality. In this paper, we adopt a more geometrical point of view, and show using Fenchel duality that any convex regularization of OT can be interpreted as ground cost adversarial. This incidentally gives access to a robust dissimilarity measure on the ground space, which can in turn be used in other applications. We propose algorithms to compute this robust cost, and illustrate the interest of this approach empirically.

Keywords: Convex Optimization · Other · General Machine Learning Techniques

Author Information

François-Pierre Paty (ENSAE Paris)
Marco Cuturi (Google)

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