[30] Alia Abbara, Antoine Baker, Florent Krzakala, and Lenka Zdeborova. On the universality of noiseless linear estimation with respect to the measurement matrix. Journal of Physics A: Mathematical and Theoretical, 53(16):164001, mar 2020.
[31] Rishabh Dudeja, Subhabrata Sen, and Yue M. Lu. Spectral universality of regularized linear regression with nearly deterministic sensing matrices. arXiv: 2208.02753, 2022.
[32] Yue M. Lu and Horng-Tzer Yau. An equivalence principle for the spectrum of random inner-product kernel matrices. arXiv: 2205.06308, 2022.
[33] Theodor Misiakiewicz. Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression. arXiv: 2204.10425, 2022.
[34] Andrea Montanari, Feng Ruan, Youngtak Sohn, and Jun Yan. The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime. arXiv: 1911.01544, 2019.
[35] Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mészard, and Lenka Zdeborová. Generalisation error in learning with random features and the hidden manifold model. In International Conference on Machine Learning, pages 3452-3462. PMLR, 2020.
[36] Ouussama Dhifallah and Yue M. Lu. A precise performance analysis of learning with random features. arXiv: 2008.11904, 2020.
[37] Tengyuan Liang and Pragya Sur. A precise high-dimensional asymptotic theory for boosting and minimum- $\ell_1$ -norm interpolated classifiers. The Annals of Statistics, 50(3):1669 - 1695, 2022.
[38] Dominik Schröder, Hugo Cui, Daniil Dmitriev, and Bruno Loureiro. Deterministic equivalent and error universality of deep random features learning. arXiv: 2302.00401, 2023.
[39] Cosme Louart, Zhenyu Liao, and Romain Couillet. A random matrix approach to neural networks. The Annals of Applied Probability, 28(2):1190-1248, 2018.
[40] Mohamed El Amine Seddik, Cosme Louart, Romain Couillet, and Mohamed Tamaazousti. The unexpected deterministic and universal behavior of large softmax classifiers. In International Conference on Artificial Intelligence and Statistics, pages 1045-1053. PMLR, 2021.
[41] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv: 1411.1784, 2014.
[42] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
[43] Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, and Matthieu Wyart. Scaling description of generalization with number of parameters in deep learning. Journal of Statistical Mechanics: Theory and Experiment, 2020(2):023401, feb 2020.
[44] Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, and Florent Krzakala. Double trouble in double descent: Bias and variance (s) in the lazy regime. In International Conference on Machine Learning, pages 2280–2290. PMLR, 2020.
[45] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv: 1708.07747, 2017.
[46] Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems, volume 20. Curran Associates, Inc., 2007.
[47] Francesco Guerra. Broken replica symmetry bounds in the mean field spin glass model. Communications in mathematical physics, 233(1):1-12, 2003.