Moritz Hardt, Ben Recht, and Yoram Singer. Train faster, generalize better: Stability of stochastic gradient descent. In Proceedings of the 33nd International Conference on Machine Learning (ICML), pages 1225-1234, New York City, NY, 2016.
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, and Sikander Randhawa. Tight analyses for non-smooth stochastic gradient descent. In Proceedings of the Conference on Learning Theory (COLT), pages 1579-1613, Phoenix, AZ, 2019.
Matthew Holland. Robustness and scalability under heavy tails, without strong convexity. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 865-873, Virtual Event, 2021.
Yegor Klochkov and Nikita Zhivotovsky. Stability and deviation optimal risk bounds with convergence rate  $o(1/n)$ . In Advances in Neural Information Processing Systems (NeurIPS), pages 5065-5076, virtual, 2021.
Vladimir Koltchinskii. Local rademacher complexities and oracle inequalities in risk minimization. The Annals of Statistics, 34(6):2593-2656, 2006.
Ilja Kuzborskij and Christoph H. Lampert. Data-dependent stability of stochastic gradient descent. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 2820-2829, Stockholm, Sweden, 2018.
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), pages 5809-5819, Virtual Event, 2020.
Yunwen Lei and Yiming Ying. Sharper generalization bounds for learning with gradient-dominated objective functions. In Proceedings of the 9th International Conference on Learning Representations (ICLR), Virtual Event, Austria, 2021.
Liam Madden, Emiliano DallAnese, and Stephen Becker. High probability convergence and uniform stability bounds for nonconvex stochastic gradient descent. arXiv preprint arXiv:2006.05610, 2020.
Llew Mason, Jonathan Baxter, Peter L. Bartlett, and Marcus R. Frean. Boosting algorithms as gradient descent. In Advances in Neural Information Processing Systems (NIPS), pages 512-518, Denver, CO, 1999.
Colin McDiarmid et al. On the method of bounded differences. Surveys in Combinatorics, 141(1): 148-188, 1989.
Nishant A. Mehta. Fast rates with high probability in exp-concave statistical learning. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1085-1093, Fort Lauderdale, FL, 2017.
Sayan Mukherjee, Partha Niyogi, Tomaso A. Poggio, and Ryan M. Rifkin. Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Adv. Comput. Math., 25(1-3):161-193, 2006.
Dheeraj Nagaraj, Prateek Jain, and Praneeth Netrapalli. SGD without replacement: Sharper rates for general smooth convex functions. In Proceedings of the 36th International Conference on Machine Learning (ICML), pages 4703-4711, Long Beach, CA, 2019.
David W. Opitz and Richard Maclin. Popular ensemble methods: An empirical study. J. Artif. Intell. Res., 11:169-198, 1999.
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan. Making gradient descent optimal for strongly convex stochastic optimization. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, UK, 2012.
William H Rogers and Terry J Wagner. A finite sample distribution-free performance bound for local discrimination rules. The Annals of Statistics, pages 506-514, 1978.