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Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness.
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Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Minimax statistical learning with wasserstein distances
Lee, J. and Raginsky, M · 2017
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
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Beery, S., Van Horn, G., and Perona, P · 2018
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Certified robustness to adversarial examples with differential privacy
Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., and Jana, S · 2019
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Shafieezadeh-Abadeh, S., Kuhn, D., and Esfahani, P. M · 2019
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Staib, M. and Jegelka, S · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Towards robust cnn-based object detection through augmentation with synthetic rain variations
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Dai, D. and Van Gool, L · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Robust empirical optimization is almost the same as mean–variance optimization
Gotoh, J.-y., Kim, M. J., and Lim, A. E · 2018
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Certifiable distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A. and Scaman, K · 2018
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Scaling provable adversarial defenses
Wong, E., Schmidt, F., Metzen, J. H., and Kolter, J. Z · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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He, P., Liu, X., Gao, J., and Chen, W · 2020
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Distributional robustness with ipms and links to regularization and gans
Husain, H · 2020
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Generalised lipschitz regularisation equals distributional robustness
Cranko, Z., Shi, Z., Zhang, X., Nock, R., and Kornblith, S · 2021
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Learning models with uniform performance via distributionally robust optimization
Duchi, J. C. and Namkoong, H · 2021
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Statistics of robust optimization: A generalized empirical likelihood approach
Duchi, J. C., Glynn, P. W., and Namkoong, H · 2021
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Generalized jensen-shannon divergence loss for learning with noisy labels
Englesson, E. and Azizpour, H · 2021
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al · 2021
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Non-asymptotic performance guarantees for neural estimation of f-divergences
Sreekumar, S., Zhang, Z., and Goldfeld, Z · 2021
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Evaluating model robustness and stability to dataset shift
Subbaswamy, A., Adams, R., and Saria, S · 2021
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Weber, M., Anand, A., Cervera-Lierta, A., Kottmann, J. S., Kyaw, T. H., Li, B., Aspuru-Guzik, A., Zhang, C., and Zhao, Z · 2021
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