Hampel, F. R., Ronchetti, E. M., Rousseeuw, P., and Stahel, W. A. Robust statistics: the approach based on influence functions. Wiley-Interscience; New York, 1986.
Hendrycks, D. and Dietterich, T. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261, 2019.
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al. The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8340-8349, 2021a.
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D. Natural adversarial examples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15262-15271, 2021b.
Huber, P. J. Robust estimation of a location parameter. In *Breakthroughs in statistics*, pp. 492-518. Springer, 1992.
Huber, P. J. Robust statistics. In International encyclopedia of statistical science, pp. 1248-1251. Springer, 2011.
Humbert, P., Le Bars, B., and Minvielle, L. Robust kernel density estimation with median-of-means principle. In International Conference on Machine Learning, pp. 9444-9465. PMLR, 2022.
Janner, M., Li, Q., and Levine, S. Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems, 34:1273-1286, 2021.
Jerrum, M. R., Valiant, L. G., and Vazirani, V. V. Random generation of combinatorial structures from a uniform distribution. Theoretical computer science, 43:169-188, 1986.
Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F. Transformers are rnns: Fast autoregressive transformers with linear attention. In International Conference on Machine Learning, pp. 5156-5165. PMLR, 2020.
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., and Shah, M. Transformers in vision: A survey. ACM Computing Surveys (CSUR), 2021.
Kim, J. and Scott, C. Robust kernel density estimation. The Journal of Machine Learning Research, 13:2529-2565, 2012.
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34, 2021.
Li, N., Liu, Y., Wu, Y., Liu, S., Zhao, S., and Liu, M. Robutrans: A robust transformer-based text-to-speech model. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8228-8235, 2020.
Lin, T., Wang, Y., Liu, X., and Qiu, X. A survey of transformers. arXiv preprint arXiv:2106.04554, 2021.
Liu, J., Singhal, T., Blessing, L. T., Wood, K. L., and Lim, K. H. Crisisbert: a robust transformer for crisis classification and contextual crisis embedding. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, pp. 133-141, 2021a.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012-10022, 2021b.
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., and Hu, H. Video swim transformer. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.