[59] Tianbao Yang, Lijun Zhang, Rong Jin, and Jinfeng Yi. Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient. In Proceedings of the 33rd International Conference on Machine Learning (ICML), pages 449-457, 2016.
[60] Lijun Zhang, Tianbao Yang, Rong Jin, and Zhi-Hua Zhou. Dynamic regret of strongly adaptive methods. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 5877-5886, 2018.
[61] Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. A simple online algorithm for competing with dynamic comparators. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), pages 390-399, 2020.
[62] Dheeraj Baby and Yu-Xiang Wang. Adaptive online estimation of piecewise polynomial trends. In Advances in Neural Information Processing Systems 33 (NeurIPS), pages 20462-20472, 2020.
[63] Peng Zhao and Lijun Zhang. Improved analysis for dynamic regret of strongly convex and smooth functions. In Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC), pages 48-59, 2021.
[64] Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC Press, 2012.
[65] Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
[66] Gilles Pisier. The Volume of Convex Bodies and Banach Space Geometry, volume 94. Cambridge University Press, 1999.
[67] Peng Zhao, Yu-Jie Zhang, Lijun Zhang, and Zhi-Hua Zhou. Adaptivity and non-stationarity: Problem-dependent dynamic regret for online convex optimization. ArXiv preprint, arXiv: 2002.07729, 2021.
[68] Mehrdad Mahdavi, Lijun Zhang, and Rong Jin. Lower and upper bounds on the generalization of stochastic exponentially concave optimization. In Proceedings of The 28th Conference on Learning Theory (COLT), pages 1305-1320, 2015.
[69] Joel A. Tropp. An introduction to matrix concentration inequalities. Foundations and Trends in Machine Learning, 8(1-2):1-230, 2015.
[70] Kwang-Sung Jun, Francesco Orabona, Stephen Wright, and Rebecca Willett. Improved strongly adaptive online learning using coin betting. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 943–951, 2017.
[71] Yoav Freund, Robert E. Schapire, Yoram Singer, and Manfred K. Warmuth. Using and combining predictors that specialize. In Proceedings of the 29th Annual ACM Symposium on the Theory of Computing (STOC), pages 334-343, 1997.
[72] Nicolò Cesa-Bianchi and Gábor Lugosi. Prediction, Learning, and Games. Cambridge University Press, 2006.
[73] Haipeng Luo and Robert E. Schapire. Achieving all with no parameters: AdaNormalHedge. In Proceedings of the 28th Annual Conference Computational Learning Theory (COLT), pages 1286-1304, 2015.
[74] Dmitry Adamskiy, Wouter M. Koolen, Alexey V. Chernov, and Vladimir Vovk. A closer look at adaptive regret. Journal of Machine Learning Research, 17:23:1-23:21, 2016.
[75] Lijun Zhang, Shiyin Lu, and Tianbao Yang. Minimizing dynamic regret and adaptive regret simultaneously. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), pages 309-319, 2020.