[14] Sebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala, Marc Mezard, and Lenka Zdeborova. The gaussian equivalence of generative models for learning with shallow neural networks. In Joan Bruna, Jan Hesthaven, and Lenka Zdeborova, editors, Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, volume 145 of Proceedings of Machine Learning Research, pages 426-471. PMLR, 16-19 Aug 2022.
[15] Noureddine El Karoui. The spectrum of kernel random matrices. The Annals of Statistics, 38(1):1-50, 2010.
[16] Hong Hu and Yue M. Lu. Universality laws for high-dimensional learning with random features. IEEE Transactions on Information Theory, pages 1-1, 2022.
[17] Andrea Montanari and Basil N. Saeed. Universality of empirical risk minimization. In Po-Ling Loh and Maxim Raginsky, editors, Proceedings of Thirty Fifth Conference on Learning Theory, volume 178 of Proceedings of Machine Learning Research, pages 4310-4312. PMLR, 02-05 Jul 2022.
[18] Bruno Loureiro, Cedric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mezard, and Lenka Zdeborova. Learning curves of generic features maps for realistic datasets with a teacher-student model. Advances in Neural Information Processing Systems, 34, 2021.
[19] Lucas Clarté, Bruno Loureiro, Florent Krzakala, and Lenka Zdeborova. Theoretical characterization of uncertainty in high-dimensional linear classification. arXiv: 2202.03295, 2022.
[20] Bruno Loureiro, Cedric Gerbelot, Maria Refinetti, Gabriele Sicuro, and Florent Krzakala. Fluctuations, bias, variance & ensemble of learners: Exact asymptotics for convex losses in high-dimension. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 14283-14314. PMLR, 17-23 Jul 2022.
[21] Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, and Lenka Zdeborova. Gaussian universality of linear classifiers with random labels in high-dimension. arXiv: 2205.13303, 2022.
[22] Luca Pesce, Florent Krzakala, Bruno Loureiro, and Ludovic Stephan. Are gaussian data all you need? extents and limits of universality in high-dimensional generalized linear estimation. arXiv preprint arXiv:2302.08923, 2023.
[23] Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, and Romain Couillet. Random matrix theory proves that deep learning representations of GAN-data behave as Gaussian mixtures. In Proceedings of the 37th International Conference on Machine Learning, ICML'20, pages 8573-8582. JMLR.org, July 2020.
[24] Lucas Clarté, Bruno Loureiro, Florent Krzakala, and Lenka Zdeborova. A study of uncertainty quantification in overparametrized high-dimensional models. arXiv: 2210.12760, 2022.
[25] Terence Tao and Van Vu. Random matrices: Universality of local eigenvalue statistics. Acta Mathematica, 206(1):127 - 204, 2011.
[26] Terence Tao and Van Vu. Random matrices: universal properties of eigenvectors. *Random Matrices: Theory and Applications*, 01(01):1150001, 2012.
[27] Ashkan Panahi and Babak Hassibi. A universal analysis of large-scale regularized least squares solutions. 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.
[28] Andrea Montanari and Phan-Minh Nguyen. Universality of the elastic net error. In 2017 IEEE International Symposium on Information Theory (ISIT), pages 2338-2342, 2017.
[29] Ehsan Abbasi, Fariborz Salehi, and Babak Hassibi. Universality in learning from linear measurements. 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. Curran Associates, Inc., 2019.