SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[GPSW17] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In International Conference on Machine Learning, pages 1321-1330. PMLR, 2017.
[GSS15] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. International Conference on Learning Representations, 2015.
[Han07] Steve Hanneke. A bound on the label complexity of agnostic active learning. In Proceedings of the 24th International Conference on Machine Learning, pages 353-360, 2007.
$\left[\mathrm{HGB}^{+}06\right]$  Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Scholkopf, and Alex Smola. Correcting sample selection bias by unlabeled data. Advances in Neural Information Processing Systems, 19, 2006.
[HK19] Steve Hanneke and Samory Kpotufe. On the value of target data in transfer learning. Advances in Neural Information Processing Systems, 32, 2019.
[HKLM20] Max Hopkins, Daniel Kane, Shachar Lovett, and Gaurav Mahajan. Noise-tolerant, reliable active classification with comparison queries. In Conference on Learning Theory, pages 1957–2006. PMLR, 2020.
[JSH20] Adel Javanmard, Mahdi Soltanolkotabi, and Hamed Hassani. Precise tradeoffs in adversarial training for linear regression. In Conference on Learning Theory, pages 2034–2078. PMLR, 2020.
[KBDG04] Daniel Kifer, Shai Ben-David, and Johannes Gehrke. Detecting change in data streams. In VLDB, volume 4, pages 180-191. Toronto, Canada, 2004.
[KKMS08] Adam Tauman Kalai, Adam R Klivans, Yishay Mansour, and Rocco A Servedio. Agnostically learning halfspaces. SIAM Journal on Computing, 37(6):1777-1805, 2008.
[KV94] Michael J Kearns and Umesh Vazirani. An introduction to computational learning theory. MIT press, 1994.
$\left[\mathrm{LAG}^{+}19\right]$  Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana. Certified robustness to adversarial examples with differential privacy. In 2019 IEEE Symposium on Security and Privacy, pages 656-672. IEEE, 2019.
[LCWC19] Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin. Certified adversarial robustness with additive noise. Advances in Neural Information Processing Systems, 32, 2019.
[LPB17] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems, 30, 2017.
[LV07] László Lovász and Santosh Vempala. The geometry of logconcave functions and sampling algorithms. Random Structures & Algorithms, 30(3):307-358, 2007.
[LWS18] Zachary Lipton, Yu-Xiang Wang, and Alexander Smola. Detecting and correcting for label shift with black box predictors. In International Conference on Machine Learning, pages 3122-3130. PMLR, 2018.
[MHS19] Omar Montasser, Steve Hanneke, and Nathan Srebro. VC classes are adversarially robustly learnable, but only improperly. In Conference on Learning Theory, pages 2512-2530. PMLR, 2019.
[MHS20] Omar Montasser, Steve Hanneke, and Nati Srebro. Reducing adversarially robust learning to non-robust pac learning. Advances in Neural Information Processing Systems, 33:14626-14637, 2020.
[ZL19] Yuchen Zhang and Percy Liang. Defending against whitebox adversarial attacks via randomized discretization. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 684-693. PMLR, 2019.
[ZLLJ19] Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan. Bridging theory and algorithm for domain adaptation. In International Conference on Machine Learning, pages 7404-7413. PMLR, 2019.
[ZLWJ20] Yuchen Zhang, Mingsheng Long, Jianmin Wang, and Michael I Jordan. On localized discrepancy for domain adaptation. arXiv preprint arXiv:2008.06242, 2020.
[ZN22] Yinglun Zhu and Robert D Nowak. Efficient active learning with abstention. In Advances in Neural Information Processing Systems, 2022.
[ZYJ+19] Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. Theoretically principled trade-off between robustness and accuracy. In International Conference on Machine Learning, pages 7472-7482. PMLR, 2019.