[CAL94] David Cohn, Les Atlas, and Richard Ladner. Improving generalization with active learning. Machine Learning, 15:201-221, 1994.
[CDG+19] Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Scott Yang. Online learning with abstention, 2019.
[Cho70] C. Chow. On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory, 16(1):41-46, 1970.
[Ni] Nicholas Carlini and David Wagner. Adversarial examples are not easily detected: Bypassing ten detection methods. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pages 3-14. ACM, 2017.
[DHM07] S. Dasgupta, D. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. In Advances in Neural Information Processing Systems 20, 2007.
[EYW10] Ran El-Yaniv and Yair Wiener. On the foundations of noise-free selective classification. Journal of Machine Learning Research, 11(5), 2010.
[EYW12] R. El-Yaniv and Y. Wiener. Active learning via perfect selective classification. Journal of Machine Learning Research, 13(2):255-279, 2012.
[FMS18] Uriel Feige, Yishay Mansour, and Robert E. Schapire. Robust inference for multiclass classification. In Firdaus Janoos, Mehryar Mohri, and Karthik Sridharan, editors, Algorithmic Learning Theory, ALT 2018, 7-9 April 2018, Lanzarote, Canary Islands, Spain, volume 83 of Proceedings of Machine Learning Research, pages 368-386. PMLR, 2018.
[FRS20] Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan. Adaptive online learning, 2020.
[GKKM20] Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, and Omar Montasser. Selective classification algorithms provably robust to arbitrary adversarial examples. In Advances in Neural Information Processing Systems, 2020.
[GKM21] Ji Gao, Amin Karbasi, and Mohammad Mahmoody. Learning and certification under instance-targeted poisoning. The Conference on Uncertainty in Artificial Intelligence (UAI), 2021.
[GSS15] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Proceedings of the 3rd International Conference on Learning Representations, ICLR, 2015.
[Han07] S. Hanneke. A bound on the label complexity of agnostic active learning. In Proceedings of the  $24^{\text{th}}$  International Conference on Machine Learning, 2007.
[Han09] S. Hanneke. Theoretical Foundations of Active Learning. PhD thesis, Machine Learning Department, School of Computer Science, Carnegie Mellon University, 2009.
[Han11] S. Hanneke. Rates of convergence in active learning. The Annals of Statistics, 39(1):333-361, 2011.
[Han12] S. Hanneke. Activized learning: Transforming passive to active with improved label complexity. Journal of Machine Learning Research, 13(5):1469-1587, 2012.
[Han14] S. Hanneke. Theory of disagreement-based active learning. Foundations and Trends in Machine Learning, 7(2-3):131-309, 2014.
[Han16] S. Hanneke. Refined error bounds for several learning algorithms. Journal of Machine Learning Research, 17(135):1-55, 2016.
[HHSY22] Nika Haghtalab, Yanjun Han, Abhishek Shetty, and Kunhe Yang. Oracle-efficient online learning for beyond worst-case adversaries. In Advances in Neural Information Processing Systems (NeurIPS), 2022. to appear.