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Many of the successes of machine learning are based on minimizing an averaged loss function.
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Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations
Schildbach, G., Fagiano, L., Frei, C., and Morari, M · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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The robust manifold defense: Adversarial training using generative models
Jalal, A., Ilyas, A., Daskalakis, C., and Dimakis, A. G · 2017
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Towards deep learning models resistant to adversarial attacks
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Improving adversarial robustness requires revisiting misclassified examples
Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., and Gu, Q · 2019
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Rademacher complexity for adversarially robust generalization
Yin, D., Kannan, R., and Bartlett, P · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., and Jordan, M · 2019
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A group-theoretic framework for data augmentation
Chen, S., Dobriban, E., and Lee, J. H · 2020
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Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Cullina, D., Bhagoji, A. N., and Mittal, P · 2018
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Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I · 2018
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Kannan, H., Kurakin, A., and Goodfellow, I · 2018
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On the geometry of adversarial examples
Khoury, M. and Hadfield-Menell, D · 2018
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Consistency of the scenario approach
Ramponi, F. A · 2018
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Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Soltanolkotabi, M., Javanmard, A., and Lee, J. D · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Su, D., Zhang, H., Chen, H., Yi, J., Chen, P.-Y., and Gao, Y · 2018
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Dobriban, E., Hassani, H., Hong, D., and Robey, A · 2020
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Achieving robustness in the wild via adversarial mixing with disentangled representations
Gowal, S., Qin, C., Huang, P.-S., Cemgil, T., Dvijotham, K., Mann, T., and Kohli, P · 2020
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Precise tradeoffs in adversarial training for linear regression
Javanmard, A., Soltanolkotabi, M., and Hassani, H · 2020
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Robust reinforcement learning via adversarial training with langevin dynamics
Kamalaruban, P., Huang, Y.-T., Hsieh, Y.-P., Rolland, P., Shi, C., and Cevher, V · 2020
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Tilted empirical risk minimization
Li, T., Beirami, A., Sanjabi, M., and Smith, V · 2020
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Efficiently learning adversarially robust halfspaces with noise
Montasser, O., Goel, S., Diakonikolas, I., and Srebro, N · 2020
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Model-based robust deep learning: Generalizing to natural, out-of-distribution data
Robey, A., Hassani, H., and Pappas, G. J · 2020
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Learning perturbation sets for robust machine learning
Wong, E. and Kolter, J. Z · 2020
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A closer look at accuracy vs. robustness
Yang, Y.-Y., Rashtchian, C., Zhang, H., Salakhutdinov, R., and Chaudhuri, K · 2020
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Consistent non-parametric methods for maximizing robustness
Bhattacharjee, R. and Chaudhuri, K · 2021
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The geometry of adversarial training in binary classification
Bungert, L., Trillos, N. G., and Murray, R · 2021
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On tilted losses in machine learning: Theory and applications
Li, T., Beirami, A., Sanjabi, M., and Smith, V · 2021
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Fixing data augmentation to improve adversarial robustness
Rebuffi, S.-A., Gowal, S., Calian, D. A., Stimberg, F., Wiles, O., and Mann, T · 2021
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Robustness between the worst and average case
Rice, L., Bair, A., Zhang, H., and Kolter, J. Z · 2021
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The dimpled manifold model of adversarial examples in machine learning
Shamir, A., Melamed, O., and BenShmuel, O · 2021
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Improving robustness of learning-based autonomous steering using adversarial images
Shen, Y., Zheng, L., Shu, M., Li, W., Goldstein, T., and Lin, M. C · 2021
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Robustart: Benchmarking robustness on architecture design and training techniques
Tang, S., Gong, R., Wang, Y., Liu, A., Wang, J., Chen, X., Yu, F., Liu, X., Song, D., Yuille, A., et al · 2021
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Do deep networks transfer invariances across classes?
Zhou, A., Tajwar, F., Robey, A., Knowles, T., Pappas, G. J., Hassani, H., and Finn, C · 2022
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