Spencer Frei, Niladri S Chatterji, and Peter Bartlett. Benign overfitting without linearity: Neural network classifiers trained by gradient descent for noisy linear data. In Conference on Learning Theory, pages 2668-2703. PMLR, 2022.
Spencer Frei, Gal Vardi, Peter L Bartlett, and Nathan Srebro. Benign overfitting in linear classifiers and leaky relu networks from kkt conditions for margin maximization. arXiv preprint arXiv:2303.01462, 2023.
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani. Surprises in high-dimensional ridgeless least squares interpolation. The Annals of Statistics, 50(2):949-986, 2022.
Lars Holst. On the lengths of the pieces of a stick broken at random. Journal of Applied Probability, 17(3):623-634, 1980.
Tianyang Hu, Jun Wang, Wenjia Wang, and Zhenguo Li. Understanding square loss in training overparametrized neural network classifiers. Advances in Neural Information Processing Systems, 35:16495-16508, 2022.
Ziwei Ji and Matus Telgarsky. Directional convergence and alignment in deep learning. Advances in Neural Information Processing Systems, 33:17176-17186, 2020.
Nirmit Joshi, Gal Vardi, and Nathan Srebro. Noisy interpolation learning with shallow univariate relu networks. arXiv preprint arXiv:2307.15396, 2023.
Frederic Koehler, Lijia Zhou, Danica J Sutherland, and Nathan Srebro. Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting. Advances in Neural Information Processing Systems, 34:20657-20668, 2021.
Yiwen Kou, Zixiang Chen, Yuzhou Chen, and Quanquan Gu. Benign overfitting for two-layer relu networks. arXiv preprint arXiv:2303.04145, 2023.
Tengyuan Liang and Alexander Rakhlin. Just interpolate: Kernel "ridgeless" regression can generalize. The Annals of Statistics, 48(3):1329-1347, 2020.
Tengyuan Liang and Benjamin Recht. Interpolating classifiers make few mistakes. Journal of Machine Learning Research, 24(20):1-27, 2023.
Kaifeng Lyu and Jian Li. Gradient descent maximizes the margin of homogeneous neural networks. In 8th International Conference on Learning Representations (ICLR), 2020.
Neil Mallinar, James Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, and Preetum Nakkiran. Benign, tempered, or catastrophic: Toward a refined taxonomy of overfitting. Advances in Neural Information Processing Systems, 35:1182-1195, 2022.
Naren Sarayu Manoj and Nathan Srebro. Interpolation learning with minimum description length. arXiv preprint arXiv:2302.07263, 2023.
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, pages 5853-5875. PMLR, 2022.
Song Mei and Andrea Montanari. The generalization error of random features regression: Precise asymptotics and the double descent curve. Communications on Pure and Applied Mathematics, 75 (4):667-766, 2022.
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 preprint 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? The Journal of Machine Learning Research, 22(1):10104-10172, 2021.
Vaishnavh Nagarajan and J Zico Kolter. Uniform convergence may be unable to explain generalization in deep learning. Advances in Neural Information Processing Systems, 32, 2019.