[22] A. Gelman and J.1/ Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Analytical Methods for Social Research. Cambridge University Press, 2006.
[23] A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, and D.B. Rubin. Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis, 2013.
[24] F. Gerace, B. Loureiro, F. Krzakala, M. Mézard, and L. Zdeborová. Generalisation error in learning with random features and the hidden manifold model. In ICML, pages 3452-3462, 2020.
[25] F. Gerace, F. Krzakala, B. Loureiro, L. Stephan, and L. Zdeborova. Gaussian universality of perceptrons with random labels. arXiv:2205.13303, 2023.
[26] C. Gerbelot and R. Berthier. Graph-based approximate message passing iterations. arXiv preprint arXiv:2109.11905, 2021.
[27] C. Gerbelot, A. Abbara, and F. Krzakala. Asymptotic errors for teacher-student convex generalized linear models (or: How to prove kabashima's replica formula). IEEE Trans. Inf. Theory, 69 (3):1824-1852, 2023.
[28] B. Ghorbani, S. Mei, T. Misiakiewicz, and A. Montanari. Limitations of lazy training of two-layers neural network. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32, 2019.
[29] J.K. Ghosh and R.V. Ramamoorthi. Bayesian Nonparametrics. Springer Series in Statistics. Springer New York, 2006.
[30] S. Goldt, M. Mezard, F. Krzakala, and L. Zdeborova. Modeling the influence of data structure on learning in neural networks: The hidden manifold model. Phys. Rev. X, 10:041044, 2020.
[31] Y. Gordon. Some inequalities for gaussian processes and applications. *Isr. J. Math.*, 50:265-289, 1985.
[32] T. Hastie, R. Tibshirani, and J.H. Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009.
[33] T. Hastie, A. Montanari, S. Rosset, and R.J. Tibshirani. Surprises in high-dimensional ridgeless least squares interpolation. Ann. Stat., 50(2):949 - 986, 2022.
[34] H. Hu and Y.M. Lu. Universality laws for high-dimensional learning with random features. arXiv:2009.07669, 2022.
[35] P.J. Huber. Robust estimation of a location parameter. Ann. Math. Stat., 35(1):73-101, 1964.
[36] A. Javanmard and A. Montanari. State evolution for general approximate message passing algorithms, with applications to spatial coupling. Inf. Inference, 2(2):115-144, 2013.
[37] G.R. Kini and C. Thrampoulidis. Phase transitions for one-vs-one and one-vs-all linear separability in multiclass gaussian mixtures. In ICASSP 2021, pages 4020-4024, 2021.
[38] V. Kothapalli, E. Rasromani, and V. Awatramani. Neural collapse: A review on modelling principles and generalization. arXiv:2206.04041, 2022.
[39] W. Krauth and M. Mézard. Storage capacity of memory networks with binary couplings. J. Physique, 50(20):3057-3066, 1989.
[40] N. Langrené, G. Lee, and Z. Zhu. Switching to non-affine stochastic volatility: A closed-form expansion for the inverse gamma model. Int. J. Theor. Appl. Finance, 19, 2015.
[41] M. Lelarge and L. Miolane. Asymptotic bayes risk for gaussian mixture in a semi-supervised setting. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pages 639-643, 2019.