Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, and Zoubin Ghahramani. Gaussian process behaviour in wide deep neural networks. In International Conference on Learning Representations (ICLR), 2018.
Andrew D. Mcrae, Santhosh Karnik, Mark Davenport, and Vidya K. Muthukumar. Harmless interpolation in regression and classification with structured features. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
Robert E. Megginson. An Introduction to Banach Space Theory. Springer-Verlag, New York, 1998.
Leon Mirsky. On the trace of matrix products. Mathematische Nachrichten, 20(3-6):171-174, 1959.
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.
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, and Anant Sahai. Classification vs regression in overparameterized regimes: Does the loss function matter? JMLR, 22(1):10104-10172, 2021.
Nicole Mücke and Ingo Steinwart. Global minima of DNNs: The plenty pantry. arXiv:1905.10686, 2019.
Radford M. Neal. Priors for Infinite Networks. Springer New York, 1996.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
Albrecht Pietsch. Eigenvalues and  $s$ -Numbers. Geest & Portig K.-G., Leipzig, 1987.
Iosif Pinelis. Exact lower and upper bounds on the incomplete gamma function. Mathematical Inequalities & Applications, 23(4):1261-1278, 2020.
Alexander Rakhlin and Xiyu Zhai. Consistency of interpolation with Laplace kernels is a high-dimensional phenomenon. In Conference on Learning Theory (COLT), 2019.
Akshay Rangamani, Lorenzo Rosasco, and Tomaso Poggio. For interpolating kernel machines, minimizing the norm of the erm solution maximizes stability. Analysis and Applications, 21:193 - 215, 2023.
Garvesh Raskutti, Martin J. Wainwright, and Bin Yu. Early stopping and non-parametric regression: An optimal data-dependent stopping rule. Journal of Machine Learning Research (JMLR), 15(11): 335-366, 2014.
Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005.
Hans Richter. Zur Abschätzung von Matrizennormen. Mathematische Nachrichten, 18(1-6):178-187, 1958.
Robert Schaback. Superconvergence of kernel-based interpolation. Journal of Approximation Theory, 235:1-19, 2018.
Cornelia Schneider and Nadine Groß. Sobolev spaces on Riemannian manifolds with bounded geometry: General coordinates traces. Mathematische Nachrichten, 286(16), 2013.
Mariia Seleznova and Gitta Kutyniok. Neural tangent kernel beyond the infinite-width limit: Effects of depth and initialization. In International Conference on Machine Learning (ICML), 2022.
Ohad Shamir. The implicit bias of benign overfitting. In Conference on Learning Theory (COLT), 2022.