Pranjal Awasthi, Natalie Frank, and Mehryar Mohri. On the rademacher complexity of linear hypothesis sets. arXiv preprint arXiv:2007.11045, 2020.
Pranjal Awasthi, Corinna Cortes, and Christopher Mohri. Theory and algorithm for batch distribution drift problems. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
Peter L Bartlett. Learning with a slowly changing distribution. In Proceedings of the fifth annual workshop on Computational Learning Theory (COLT), 1992.
Peter L Bartlett, Shai Ben-David, and Sanjeev R Kulkarni. Learning changing concepts by exploiting the structure of change. Machine Learning, 41(2):153-174, 2000.
Rakesh D Barve and Philip M Long. On the complexity of learning from drifting distributions. In Conference on Computational Learning Theory (COLT), 1996.
Rakesh D Barve and Philip M Long. On the complexity of learning from drifting distributions. Information and Computation, 138(2):170-193, 1997.
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. A theory of learning from different domains. Machine learning, 79(1):151-175, 2010.
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi. Introduction to statistical learning theory. In Summer School on Machine Learning. Springer, 2003.
Andrew R Conn, Nicholas IM Gould, and Philippe L Toint. Trust region methods. SIAM, 2000.
Koby Crammer, Eyal Even-Dar, Yishay Mansour, and Jennifer Wortman Vaughan. Regret minimization with concept drift. Conference on Learning Theory (COLT), 2010.
Yoav Freund and Yishay Mansour. Learning under persistent drift. In European Conference on Computational Learning Theory, pages 109-118, 1997.
William W Hager. Minimizing a quadratic over a sphere. SIAM Journal on Optimization, 12(1): 188-208, 2001.
Steve Hanneke and Liu Yang. Statistical learning under nonstationary mixing processes. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
Steve Hanneke, Varun Kanade, and Liu Yang. Learning with a drifting target concept. In Conference on Algorithmic Learning Theory (ALT), 2015.
David P Helmbold and Philip M Long. Tracking drifting concepts using random examples. In Proceedings of the fourth annual workshop on Computational Learning Theory (COLT), 1991.
David P Helmbold and Philip M Long. Tracking drifting concepts by minimizing disagreements. Machine learning, 14(1):27-45, 1994.
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari. On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. Advances in Neural Information Processing Systems (NeurIPS), 2008.
Philip M Long. The complexity of learning according to two models of a drifting environment. In Conference on Computational Learning Theory (COLT), 1998.
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. Domain adaptation: Learning bounds and algorithms. arXiv preprint arXiv:0902.3430, 2009.
Alessio Mazzetto and Eli Upfal. Nonparametric density estimation under distribution drift. International Conference on Machine Learning (ICML), 2023.
Mehryar Mohri and Andres Muñoz Medina. New analysis and algorithm for learning with drifting distributions. In International Conference on Algorithmic Learning Theory (ALT), 2012.