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Free Energy of Multi-Layer Generalized Linear Models

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Abstract

We compute the high-dimensional limit of the free energy associated with a multi-layer generalized linear model. Under certain technical assumptions, we identify the limit in terms of a variational formula. The approach is to first show that the limit is a solution to a Hamilton–Jacobi equation whose initial condition is related to the limiting free energy of a model with one fewer layer. Then, we conclude with an iteration.

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References

  1. Barbier, J., Krzakala, F., Macris, N., Miolane, L., Zdeborová, L.: Optimal errors and phase transitions in high-dimensional generalized linear models. Proc. Natl. Acad. Sci. 116(12), 5451–5460 (2019)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  2. Barbier, J., Macris, N.: The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference. Probab. Theory Relat. Fields 174(3–4), 1133–1185 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  3. Barbier, J., Macris, N.: The adaptive interpolation method for proving replica formulas. Applications to the Curie–Weiss and Wigner spike models. J. Phys. A Math. Theor. 52(29), 294002 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  4. Barbier, J., Macris, N., Miolane, L.: The layered structure of tensor estimation and its mutual information. In: 55th Annual Allerton Conference on Communication, Control, and Computing, pp. 1056–1063. IEEE, (2017)

  5. Bardi, M., Evans, L.C.: On Hopf’s formulas for solutions of Hamilton–Jacobi equations. Nonlinear Anal. Theory Methods Appl. 8(11), 1373–1381 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  6. Barra, A., Dal Ferraro, G., Tantari, D.: Mean field spin glasses treated with PDE techniques. Eur. Phys. J. B 86(7), 1–10 (2013)

    Article  MathSciNet  Google Scholar 

  7. Barra, A., Di Biasio, A., Guerra, F.: Replica symmetry breaking in mean-field spin glasses through the Hamilton–Jacobi technique. J. Stat. Mech. Theory Exp. 2010(09), P09006 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Boucheron, S., Lugosi, G., Massart, P.: Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press, Oxford (2013)

    Book  MATH  Google Scholar 

  9. Chen, H.-B.: Hamilton–Jacobi equations for nonsymmetric matrix inference. arXiv preprint arXiv:2006.05328 (2020)

  10. Chen, H.-B., Mourrat, J.-C., Xia, J.: Statistical inference of finite-rank tensors. arXiv preprint arXiv:2104.05360 (2021)

  11. Chen, H.-B., Xia, J.: Fenchel–Moreau identities on self-dual cones. arXiv preprint arXiv:2011.06979 (2020)

  12. Chen, H.-B., Xia, J.: Hamilton–Jacobi equations for inference of matrix tensor products. arXiv preprint arXiv:2009.01678 (2020)

  13. Dia, M., Macris, N., Krzakala, F., Lesieur, T., Zdeborová, L., et al.: Mutual information for symmetric rank-one matrix estimation: a proof of the replica formula. Adv. Neural Inf. Process. Syst. 29, 424–432 (2016)

    Google Scholar 

  14. Evans, L.C.: Partial Differential Equations, vol. 19. American Mathematical Society, Providence (2010)

    MATH  Google Scholar 

  15. Gabrié, M., Manoel, A., Luneau, C., Barbier, J., Macris, N., Krzakala, F., Zdeborová, L.: Entropy and mutual information in models of deep neural networks. J. Stat. Mech. Theory Exp. 2019(12), 124014 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  16. Genovese, G., Barra, A.: A mechanical approach to mean field spin models. J. Math. Phys. 50(5), 053303 (2009)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  17. Guerra, F.: Sum rules for the free energy in the mean field spin glass model. Fields Inst. Commun. 30(11), 161–170 (2001)

    MathSciNet  MATH  Google Scholar 

  18. Lions, P.-L., Rochet, J.-C.: Hopf formula and multitime Hamilton–Jacobi equations. Proc. Am. Math. Soc. 96(1), 79–84 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  19. Luneau, C., Barbier, J., Macris, N.: Mutual information for low-rank even-order symmetric tensor estimation. Inf. Inference A J. IMA 10(4), 1167–1207. arXiv:1904.04565 (2020). https://doi.org/10.1093/imaiai/iaaa022

  20. Luneau, C., Macris, N., Barbier, J.: High-dimensional rank-one nonsymmetric matrix decomposition: the spherical case. arXiv preprint arXiv:2004.06975 (2020)

  21. Mourrat, J.-C.: Free energy upper bound for mean-field vector spin glasses. arXiv preprint arXiv:2010.09114 (2020)

  22. Mourrat, J.-C.: Hamilton–Jacobi equations for mean-field disordered systems. Ann. Henri Lebesgue 4, 453–484 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  23. Mourrat, J.-C.: Nonconvex interactions in mean-field spin glasses. Probab. Math. Phys. 2(2), 61–119 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mourrat, J.-C.: The Parisi formula is a Hamilton–Jacobi equation in Wasserstein space. Can. J. Math. 3, 1–23 (2021)

    Google Scholar 

  25. Mourrat, J.-C.: Hamilton–Jacobi equations for finite-rank matrix inference. Ann. Appl. Probab. 30(5), 2234–2260 (2020). https://doi.org/10.1214/19-AAP1556. https://projecteuclid.org/journals/annals-of-applied-probability/volume-30/issue-5/HamiltonJacobi-equations-for-finite-rank-matrix-inference/10.1214/19-AAP1556.short@article%7B10.1214/19-AAP1556

  26. Mourrat, J.-C., Panchenko, D.: Extending the Parisi formula along a Hamilton–Jacobi equation. Electron. J. Probab. 25, 1–17 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  27. Reeves, G.: Information-theoretic limits for the matrix tensor product. IEEE J. Sel. Areas Inf. Theory 1(3), 777–798 (2020)

    Article  Google Scholar 

  28. Rockafellar, R.T.: Convex Analysis, vol. 36. Princeton University Press, Princeton (1970)

    Book  MATH  Google Scholar 

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Acknowledgements

We warmly thank Jean-Christophe Mourrat for many helpful comments and discussions. We thank the referees for the comments that help us improve the paper considerably.

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Correspondence to Jiaming Xia.

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Communicated by S. Chatterjee.

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Chen, HB., Xia, J. Free Energy of Multi-Layer Generalized Linear Models. Commun. Math. Phys. 400, 1861–1913 (2023). https://doi.org/10.1007/s00220-022-04630-4

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