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We consider the sample complexity of learning with adversarial robustness.
Adversarial examples for non-parametric methods: Attacks, defenses and large sample limits
Yang, Y., Rashtchian, C., Wang, Y., and Chaudhuri, K · 1906
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Statistical Learning Theory
Vapnik, V. N · 1998
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Large margin classification using the perceptron algorithm
Freund, Y. and Schapire, R. E · 1999
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Understanding and mitigating the tradeoff between robustness and accuracy
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J. C., and Liang, P · 2002
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When are non-parametric methods robust?
Bhattacharjee, R. and Chaudhuri, K · 2003
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Black-box certification and learning under adversarial perturbations
Ashtiani, H., Pathak, V., and Urner, R · 2006
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Sharp statistical guarantees for adversarially robust gaussian classification
Dan, C., Wei, Y., and Ravikumar, P · 2006
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Provable tradeoffs in adversarially robust classification
Dobriban, E., Hassani, H., Hong, D., and Robey, A · 2006
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The complexity of adversarially robust proper learning of halfspaces with agnostic noise
Diakonikolas, I., Kane, D. M., and Manurangsi, P · 2007
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
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The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P. D., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P. D., Wu, X., Jha, S., and Swami, A · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. A · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
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Towards proving the adversarial robustness of deep neural networks
Katz, G., Barrett, C. W., Dill, D. L., Julian, K., and Kochenderfer, M. J · 2017
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Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2017
Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
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Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J. C · 2018
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Analyzing the robustness of nearest neighbors to adversarial examples
Wang, Y., Jha, S., and Chaudhuri, K · 2018
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Improved generalization bounds for robust learning
Attias, I., Kontorovich, A., and Mansour, Y · 2019
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Lower bounds on adversarial robustness from optimal transport
Bhagoji, A. N., Cullina, D., and Mittal, P · 2019
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VC classes are adversarially robustly learnable, but only improperly
Montasser, O., Hanneke, S., and Srebro, N · 2019
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Cited alongside, same era.
Practical black-box attacks against deep learning systems using adversarial examples
Papernot, N., McDaniel, P. D., Goodfellow, I. J., Jha, S., Celik, Z. B., and Swami, A · 2017
Cited alongside, same era.
Pac-learning in the presence of adversaries
Cullina, D., Bhagoji, A. N., and Mittal, P · 2018
Cited alongside, same era.
Adversarial risk bounds for binary classification via function transformation
Khim, J. and Loh, P · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
Cited alongside, same era.
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
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Rademacher complexity for adversarially robust generalization
Yin, D., Ramchandran, K., and Bartlett, P. L · 2019
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A closer look at accuracy vs. robustness, 2020
Yang, Y.-Y., Rashtchian, C., Zhang, H., Salakhutdinov, R., and Chaudhuri, K · 2020
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