Hassan Ashtiani, Vinayak Pathak, and Ruth Urner. Black-box certification and learning under adversarial perturbations. In Proceedings of the 37th International Conference on Machine Learning, ICML, pages 388-398, 2020.
Hassan Ashtiani, Vinayak Pathak, and Ruth Urner. Adversarially robust learning with tolerance. In International Conference on Algorithmic Learning Theory, ALT, pages 115-135, 2023.
Idan Attias and Steve Hanneke. Adversarially robust pac learnability of real-valued functions. arXiv preprint arXiv:2206.12977, 2022.
Idan Attias, Aryeh Kontorovich, and Yishay Mansour. Improved generalization bounds for robust learning. In Algorithmic Learning Theory, ALT, pages 162-183, 2019.
Idan Attias, Steve Hanneke, and Yishay Mansour. A characterization of semi-supervised adversarially robust pac learnability. Advances in Neural Information Processing Systems, 35:23646-23659, 2022.
Pranjal Awasthi, Abhratanu Dutta, and Aravindan Vijayaraghavan. On robustness to adversarial examples and polynomial optimization. In Advances in Neural Information Processing Systems, NeurIPS, pages 13760-13770, 2019.
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong. H-consistency bounds for surrogate loss minimizers. In International Conference on Machine Learning, pages 1117-1174. PMLR, 2022a.
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong. Multi-class  $h$ -consistency bounds. Advances in neural information processing systems, 35:782-795, 2022b.
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong. Theoretically grounded loss functions and algorithms for adversarial robustness. In International Conference on Artificial Intelligence and Statistics, pages 10077-10094. PMLR, 2023.
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, and Hongyang Zhang. On the power of abstention and data-driven decision making for adversarial robustness. CoRR, abs/2010.06154, 2020.
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, and Dravyansh Sharma. Reliable learning for test-time attacks and distribution shift. CoRR, abs/2304.03370, 2023.
Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9(59):1823-1840, 2008.
Robi Bhattacharjee and Kamalika Chaudhuri. Consistent non-parametric methods for maximizing robustness. In Advances in Neural Information Processing Systems 34 NeurIPS, pages 9036-9048, 2021.
Robi Bhattacharjee, Somesh Jha, and Kamalika Chaudhuri. Sample complexity of robust linear classification on separated data. In International Conference on Machine Learning, pages 884-893. PMLR, 2021.
Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, and Kamalika Chaudhuri. Robust empirical risk minimization with tolerance. In International Conference on Algorithmic Learning Theory ALT, pages 182-203, 2023.
Sadia Chowdhury and Ruth Urner. Robustness should not be at odds with accuracy. In 3rd Symposium on Foundations of Responsible Computing, FORC, pages 5:1-5:20, 2022.
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal. Pac-learning in the presence of adversaries. In Advances in Neural Information Processing Systems, NeurIPS, pages 230-241, 2018.
Ran El-Yaniv and Yair Wiener. On the foundations of noise-free selective classification. J. Mach. Learn. Res., 11:1605-1641, 2010.