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Optimal transportation plans with escort entropy regularization

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Abstract

The entropy-regularized Wasserstein transportation problem is useful for solving lots of problems in various applications. We study the deformed escort entropy regularization including the q-escort regularization and prove that the family of the inverse escort distributions of the optimal transportation plans forms a deformed exponential family, which has dually flat information-geometric structure. This elucidates the role of the escort transformation and its inverse in the theory of deformed exponential families. We further prove that the regularized cost function gives the dual potential of the flat manifold. We derive a new divergence function between two probability distributions in a probability simplex and a related Riemannian metric based on the regularized cost.

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References

  1. Amari, S.: Information Geometry and Its Applications. Springer, Berlin (2016)

    Book  Google Scholar 

  2. Amari, S., Karakida, R., Oizumi, M.: Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem. Inf. Geom. 1, 13–37 (2018)

    Article  MathSciNet  Google Scholar 

  3. Amari, S., Karakida, R., Oizumi, M., Cuturi, M.: Information geometry for regularized optimal transport and barycenters of patterns. Neural Comput. 31, 827–848 (2019)

    Article  MathSciNet  Google Scholar 

  4. Amari, S., Ohara, A., Matsuzoe, H.: Geometry of deformed exponential families: invariant, dually-flat and conformal geometries. Phys. A 391, 4308–4319 (2012)

    Article  MathSciNet  Google Scholar 

  5. Ay, N., Jost, J., Le, H.V., Schwachhöfer, L.: Information Geometry. Springer, Berlin (2017)

    Book  Google Scholar 

  6. Cuturi, M.: Sinkhorn distances: light speed computation of optimal transport. In: Advances in Neural Information Processing Systems 2292–2300 (2013)

  7. Cuturi, M., Peyré, G.: A smoothed dual formulation for variational Wasserstein problems. SIAM J. Imaging Sci. 9 (2016)

  8. Feydy, J., Sehourne, T., Vialard, F.-X., Amari, S., Trouve, A., Peyre, G.: Interpolating between optimal transport and MMD using Sinkhorn divergences. PMLR 89 (AISTATS’19), 2681–2690 (2019)

  9. Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M., Poggio, T.: Learning with a Wasserstein loss. Adv. Neural Inf. Process. Syst. (NIPS) 28 (2015)

  10. Genevay, A., Peyre, G., Cuturi, M.: Learning generative models with Sinkhorn divergences. PMLR 84 (AISTATS’18), 1608–1617 (2018)

  11. Khan, G. and Zhang, J.: The Kähler geometry of certain optimal transport problems. ar**v:1812.00032v4 (2019)

  12. Li, W., Zhao, Li.: Wasserstein information matrix (2020) (To appear)

  13. Montavon, G., Muller, K., Cuturi, M.: Wasserstein training for Boltzmann machines. ar**v:1507.01972v1 (2015)

  14. Muzellec, B., Nock, R., Patrini, G., Nielsen, F.: Tsallis regularized optimal transport and ecological inference. ar**v:1609.04495v1 (2016)

  15. Naudts, J.: Estimators escort probabilities and \(phi\)-exponential families in statistical physics. J. Inequal. Pure Appl. Math. 5, 122 (2004)

    MATH  Google Scholar 

  16. Naudts, J.: Generalized Thermostatistics. Springer, Berlin (2011)

    Book  Google Scholar 

  17. Naudts, J., Zhang, J.: \(\rho \)-\(\tau \) embedding and gauge freedom in information geometry. Inf. Geom. 1, 79–115 (2018)

    Article  MathSciNet  Google Scholar 

  18. Peyré, G., Cuturi, M.: Computational optimal transport: with applications to data science. Found. Trends Theor. Comput. Sci. 11, 355–607 (2019)

  19. Santambrogio, F.: Optimal Transport for Applied Mathematicians. Birkhauser, Basel (2015)

    Book  Google Scholar 

  20. Tsallis, C.: Introduction to Non-extensive Statistical Mechanics: Approaching a Complex World. Springer, Berlin (2009)

    MATH  Google Scholar 

  21. Villani, C.: Topics in Optimal Transportation. Graduate Studies in Mathematics. AMS, Providence (2003)

    MATH  Google Scholar 

  22. Villani, C.: Optimal Transport: Old and New. Springer, Berlin (2008)

    MATH  Google Scholar 

  23. Wong, T.-K.L.: Logarithmic divergence from optimal transport and Renyi geometry. Inf. Geom. 1, 39–78 (2018)

    Article  MathSciNet  Google Scholar 

  24. Wong, T.-K.L., Yang, J.: Pseudo-Riemannian geometry embeds information geometry in optimal transport. ar**v:1906.00030v4 (2021)

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Correspondence to Shun-ichi Amari.

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Kurose, T., Yoshizawa, S. & Amari, Si. Optimal transportation plans with escort entropy regularization. Info. Geo. 5, 79–95 (2022). https://doi.org/10.1007/s41884-021-00058-2

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  • DOI: https://doi.org/10.1007/s41884-021-00058-2

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