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A growing body of research has shown that many classifiers are susceptible to {\em{adversarial examples}} -- small strategic modifications to test inputs that lead to misclassification.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Yang, G., Li, J., Zhang, P., Zhang, H., Razenshteyn, I. P., and Bubeck, S · 1906
Earlier work this paper cites.
Adversarial examples for non-parametric methods: Attacks, defenses and large sample limits
Yang, Y., Rashtchian, C., Wang, Y., and Chaudhuri, K · 1906
Earlier work this paper cites.
The condensed nearest neighbor rule (corresp.)
Hart, P. E · 1968
Earlier work this paper cites.
The reduced nearest neighbor rule (corresp.)
Gates, G. W · 1972
Earlier work this paper cites.
Consistent nonparametric regression
Stone, C · 1977
Earlier work this paper cites.
A Probabilistic Theory of Pattern Recognition , volume 31 of Stochastic Modelling and Applied Probability
Devroye, L., Györfi, L., and Lugosi, G · 1996
Earlier work this paper cites.
Adversarial learning
Lowd, D. and Meek, C · 2005
Earlier work this paper cites.
Consistency of support vector machines and other regularized kernel classifiers
Steinwart, I · 2005
Earlier work this paper cites.
Explaining and harnessing adversarial examples, March 20 2014
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Near-optimal sample compression for nearest neighbors
Gottlieb, L., Kontorovich, A., and Nisnevitch, P · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
Earlier work this paper cites.
Evasion and hardening of tree ensemble classifiers
Kantchelian, A., Tygar, J. D., and Joseph, A. D · 2015
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A bayes consistent 1-nn classifier
Kontorovich, A. and Weiss, R · 2015
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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
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P. D., Wu, X., Jha, S., and Swami, A · 2016
Cited alongside, same era.
The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality
Amsaleg, L., Bailey, J., Barbe, D., Erfani, S. M., Houle, M. E., Nguyen, V., and Radovanovic, M · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
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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
Later among the works it cites.
Provably robust boosted decision stumps and trees against adversarial attacks
Andriushchenko, M. and Hein, M · 2019
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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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Nearest-neighbor sample compression: Efficiency, consistency, infinite dimensions
Kontorovich, A., Sabato, S., and Weiss, R · 2017
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Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2017
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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
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Adversarially robust submodular maximization under knapsack constraints
Avdiukhin, D., Mitrovic, S., Yaroslavtsev, G., and Zhou, S · 2019
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Robust decision trees against adversarial examples
Chen, H., Zhang, H., Boning, D. S., and Hsieh, C.-J · 2019
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Universal bayes consistency in metric spaces
Hanneke, S., Kontorovich, A., Sabato, S., and Weiss, R · 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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On the robustness of deep k-nearest neighbors
Sitawarin, C. and Wagner, D. A · 2019
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