[32] Daniel LeJeune, Hamid Javadi, and Richard Baraniuk. The implicit regularization of ordinary least squares ensembles. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 3525-3535. PMLR, 26-28 Aug 2020.
[33] Pratik Patil and Daniel LeJeune. Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning, 2023.
[34] Jin-Hong Du, Pratik Patil, and Arun Kumar Kuchibhotla. Subsample ridge ensembles: Equivalences and generalized cross-validation, 2023.
[35] Pratik Patil, Jin-Hong Du, and Arun Kumar Kuchibhotla. Bagging in overparameterized learning: Risk characterization and risk monotonization, 2022.
[36] Alexander Atanasov, Blake Bordelon, Sabarish Sainathan, and Cengiz Pehlevan. The onset of variance-limited behavior for networks in the lazy and rich regimes. In *The Eleventh International Conference on Learning Representations*, 2023.
[37] Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan. Out-of-distribution generalization in kernel regression. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 12600-12612. Curran Associates, Inc., 2021.
[38] Peter Sollich and Anders Krogh. Learning with ensembles: How overfitting can be useful. Advances in neural information processing systems, 8, 1995.
[39] 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. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[40] Devon Janke and David V. Anderson. Analyzing the effects of noise and variation on the accuracy of analog neural networks. In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, August 2020.
[41] A. Aldo Faisal, Luc P. J. Selen, and Daniel M. Wolpert. Noise in the nervous system. Nature Reviews Neuroscience, 9(4):292-303, April 2008.
[42] Jack Sherman and Winifred J. Morrison. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. The Annals of Mathematical Statistics, 21(1):124-127, March 1950.
[43] Preetum Nakkiran, Prayaag Venkat, Sham Kakade, and Tengyu Ma. Optimal regularization can mitigate double descent. arXiv preprint arXiv:2003.01897, 2020.
[44] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[45] J Howard. Imagenette. Accessed: 25-10-2025.
[46] Saining Xie, Ross Girshick, Piotr Dólár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks, 2017.
[47] Diego Doimo, Aldo Glielmo, Sebastian Goldt, and Alessandro Laio. Redundant representations help generalization in wide neural networks. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
[48] Alexander Maloney, Daniel A. Roberts, and James Sully. A solvable model of neural scaling laws, 2022.
[49] Blake Bordelon and Cengiz Pehlevan. Population codes enable learning from few examples by shaping inductive bias. eLife, 11, December 2022.