[18] B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” The Annals of Statistics, vol. 32, no. 2, pp. 407–499, 2004.
[19] T. Hastie, R. Tibshirani, and J. Friedman, Elements of Statistical Learning, Second. Springer Science & Business Media, 2009.
[20] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, "Understanding deep learning requires rethinking generalization," in International Conference on Learning Representations, 2017.
[21] P. L. Bartlett, A. Montanari, and A. Rakhlin, “Deep learning: A statistical viewpoint,” arXiv:2103.09177, 2021.
[22] P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler, “Benign overfitting in linear regression,” Proceedings of the National Academy of Sciences, vol. 117, no. 48, pp. 30063-30070, 2020.
[23] F. Koehler, L. Zhou, D. J. Sutherland, and N. Srebro, "Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting," in NeurIPS, 2021.
[24] M. Belkin, D. Hsu, S. Ma, and S. Mandal, "Reconciling modern machine-learning practice and the classical bias-variance trade-off," Proceedings of the National Academy of Sciences, vol. 116, no. 32, pp. 15849-15854, 2019.
[25] M. Belkin, D. Hsu, and J. Xu, “Two Models of Double Descent for Weak Features,” SIAM Journal on Mathematics of Data Science, vol. 2, no. 4, pp. 1167–1180, 2020.
[26] T. Hastie, A. Montanari, S. Rosset, and R. J. Tibshirani, "Surprises in high-dimensional ridgeless least squares interpolation," The Annals of Statistics, vol. 50, no. 2, pp. 949-986, 2022.
[27] C. Dan, Y. Wei, and P. Ravikumar, "Sharp statistical guarantees for adversarially robust Gaussian classification," in Proceedings of the 37th international conference on machine learning, ser. Proceedings of machine learning research, vol. 119, PMLR, 2020, pp. 2345-2355.
[28] E. Dobriban, H. Hassani, D. Hong, and A. Robey, Provable tradeoffs in adversarially robust classification, 2022.
[29] A. Javanmard and M. Soltanolkotabi, "Precise statistical analysis of classification accuracies for adversarial training," The Annals of Statistics, vol. 50, no. 4, pp. 2127-2156, 2022.
[30] R. Tibshirani, "Regression shrinkage and selection via the LASSO," Journal of the Royal Statistical Society. Series B (Methodological), pp. 267-288, 1996.
[31] M. J. Wainwright, High-Simensional Statistics: a non-Asymptotic Viewpoint, ser. Cambridge series on statistical and probabilistic mathematics 48. Cambridge University Press, 2019.
[32] A. Belloni, V. Chernozhukov, and L. Wang, "Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming," Biometrika, vol. 98, no. 4, pp. 791-806, 2011.
[33] H. Xu, C. Caramanis, and S. Mannor, "Robust regression and lasso," Advances in Neural Information Processing Systems, vol. 21, 2008.
[34] ——, “Robustness and regularization of support vector machines,” Journal of Machine Learning Research, vol. 10, no. 51, pp. 1485–1510, 2009.
[35] Y. Xing, Q. Song, and G. Cheng, "On the Generalization Properties of Adversarial Training," in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2021, pp. 505-513.
[36] D. P. Bertsekas, A. Nedi, and A. E. Ozdaglar, Convex Analysis and Optimization. 2003.
[37] F. H. Clarke, Optimization and Nonsmooth Analysis. Society for Industrial and Applied Mathematics, 1990.
[38] S. Boyd, J. Duchi, M. Pilanci, and L. Vandenberghe, "Subgradients (Lecture Notes)," Tech. Rep. Notes for EE364b, Stanford University, Spring 2021-22, 2022.
[39] S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 33-61, 1998.
[40] F. Bach, "High-dimensional analysis of double descent for linear regression with random projections," arXiv:2303.01372, 2023.
[41] R. J. Tibshirani, “The Lasso Problem and Uniqueness,” Electronic Journal of Statistics, vol. 7, pp. 1456–1490, 2013.
[42] S. Diamond and S. Boyd, "CVXPY: A Python-embedded modeling language for convex optimization," Journal of Machine Learning Research, vol. 17, no. 83, pp. 1-5, 2016.
[43] A. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines," in Advances in Neural Information Processing Systems 20, 2008, pp. 1177-1184.
[44] M. F. Scott, N. Fradgley, A. R. Bentley, et al., "Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding," Genome Biology, vol. 22, no. 1, p. 137, 2021.