[29] Vladimir Koltchinskii and Dmitriy Panchenko. Rademacher processes and bounding the risk of function learning. In High dimensional probability II, pages 443-457. Springer, 2000.
[30] Ilja Kuzborskij and Csaba Szepesvári. Nonparametric regression with shallow overparameterized neural networks trained by GD with early stopping. In Mikhail Belkin and Samory Kpotufe, editors, Conference on Learning Theory, COLT 2021, 15-19 August 2021, Boulder, Colorado, USA, volume 134 of Proceedings of Machine Learning Research, pages 2853-2890. PMLR, 2021. URL http://proceedings.mlr.press/v134/kuzborskij21a.html.
[31] Yunwen Lei and Yiming Ying. Fine-grained analysis of stability and generalization for stochastic gradient descent. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 5809-5819. PMLR, 2020. URL http://proceedings.mlr.press/v119/lei20c.html.
[32] Jian Li, Xuanyuan Luo, and Mingda Qiao. On generalization error bounds of noisy gradient methods for non-convex learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL https://openreview.net/forum?id=SkxxtgHKPS.
[33] Jiangyuan Li, Thanh V. Nguyen, Chinmay Hegde, and Ka Wai Wong. Implicit sparse regularization: The impact of depth and early stopping. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 28298-28309, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/ ee188463935a061dee6df8bf449cb882-AAbstract.html.
[34] Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak. Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks. In Silvia Chiappa and Roberto Calandra, editors, The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy], volume 108 of Proceedings of Machine Learning Research, pages 4313-4324. PMLR, 2020. URL http://proceedings.mlr.press/v108/1i20j.html.
[35] Zhu Li, Weijie Su, and Dino Sejdinovic. Benign overfitting and noisy features. CoRR, abs/2008.02901, 2020. URL https://arxiv.org/abs/2008.02901.
[36] Junhong Lin and Volkan Cevher. Optimal rates for spectral-regularized algorithms with least-squares regression over hilbert spaces. CoRR, abs/1801.06720, 2018. URL http://arxiv.org/abs/1801.06720.
[37] Junhong Lin and Lorenzo Rosasco. Optimal rates for multi-pass stochastic gradient methods. J. Mach. Learn. Res., 18:97:1-97:47, 2017. URL http://jmlr.org/papers/v18/17-176.html.
[38] Chaoyue Liu, Amirhesam Abedsoltan, and Mikhail Belkin. On emergence of clean-priority learning in early stopped neural networks. CoRR, abs/2306.02533, 2023. doi: 10.48550/arXiv.2306.02533. URL https://doi.org/10.48550/arXiv.2306.02533.
[39] Kaifeng Lyu and Jian Li. Gradient descent maximizes the margin of homogeneous neural networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL https://openreview.net/forum?id=SJeLIgBKPS.
[40] Kaifeng Lyu, Zhiyuan Li, Runzhe Wang, and Sanjeev Arora. Gradient descent on two-layer nets: Margin maximization and simplicity bias. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 12978-12991, 2021. URL https://proceedings.neurips.cc/paper/2021/bit/6c351da15b5e8a743a21ee96a86e25df-Abstract.html.