Martin Anthony and Peter L. Bartlett. Neural Network Learning - Theoretical Foundations. Cambridge University Press, 2002. ISBN 978-0-521-57353-5.
Peter L Bartlett and Shahar Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3(Nov):463-482, 2002.
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky. Spectrally-normalized margin bounds for neural networks. Advances in neural information processing systems, 30, 2017.
Amit Daniely and Elad Granot. Generalization bounds for neural networks via approximate description length. Advances in Neural Information Processing Systems, 32, 2019.
Amit Daniely and Elad Granot. On the sample complexity of two-layer networks: Lipschitz vs. element-wise lipschitz activation. CoRR, abs/2211.09634, 2022. doi: 10.48550/arXiv.2211.09634.
Amit Daniely, Sivan Sabato, Shai Ben-David, and Shai Shalev-Shwartz. Multiclass learnability and the erm principle. In Proceedings of the 24th Annual Conference on Learning Theory, pages 207-232. JMLR Workshop and Conference Proceedings, 2011.
Simon Du and Jason Lee. On the power of over-parametrization in neural networks with quadratic activation. In International conference on machine learning, pages 1329-1338. PMLR, 2018.
Vitaly Feldman. Generalization of erm in stochastic convex optimization: The dimension strikes back. Advances in Neural Information Processing Systems, 29, 2016.
Margalit Glasgow, Colin Wei, Mary Wootters, and Tengyu Ma. Max-margin works while large margin fails: Generalization without uniform convergence. In The Eleventh International Conference on Learning Representations, 2022.
Noah Golowich, Alexander Rakhlin, and Ohad Shamir. Size-independent sample complexity of neural networks. In Conference On Learning Theory, COLT 2018, volume 75 of Proceedings of Machine Learning Research, pages 297-299. PMLR, 2018.
Sham M. Kakade, Karthik Sridharan, and Ambuj Tewari. On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. In Advances in Neural Information Processing Systems 21, 2008.
Kaifeng Lyu and Jian Li. Gradient descent maximizes the margin of homogeneous neural networks. arXiv preprint arXiv:1906.05890, 2019.
Andreas Maurer. A vector-contraction inequality for rademacher complexities. In Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings 27, pages 3-17. Springer, 2016.
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018.
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.
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro. Norm-based capacity control in neural networks. In Conference on learning theory, pages 1376-1401. PMLR, 2015.
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro. A pac-bayesian approach to spectrally-normalized margin bounds for neural networks. arXiv preprint arXiv:1707.09564, 2017.
Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning - From Theory to Algorithms. Cambridge University Press, 2014. ISBN 978-1-10-705713-5.
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan. Learnability, stability and uniform convergence. The Journal of Machine Learning Research, 11:2635-2670, 2010.
Nathan Srebro and Karthik Sridharan. Note on refined dudley integral covering number bound. URL https://www.cs.cornell.edu/~sridharan/dudley.pdf.
Gal Vardi, Ohad Shamir, and Nathan Srebro. The sample complexity of one-hidden-layer neural networks. CoRR, abs/2202.06233, 2022.