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We formally study the problem of classification under adversarial perturbations from a learner's perspective as well as a third-party who aims at certifying the robustness of a given black-box classifier.
On the uniform convergence of relative frequencies of events to their probabilities
Vapnik, V. N. and Chervonenkis, A. Y · 1971
Earlier work this paper cites.
A theory of the learnable
Valiant, L. G · 1984
Earlier work this paper cites.
Relating data compression and learnability
Littlestone, N. and Warmuth, M · 1986
Earlier work this paper cites.
epsilon-nets and simplex range queries
Haussler, D. and Welzl, E · 1987
Earlier work this paper cites.
Learnability and the vapnik-chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. K · 1989
Earlier work this paper cites.
The strength of weak learnability
Schapire, R. E · 1990
Earlier work this paper cites.
Learning by distances
Ben-David, S., Itai, A., and Kushilevitz, E · 1995
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Kearns, M. J · 1998
Earlier work this paper cites.
Boosting: Foundations and algorithms
Schapire, R. E. and Freund, Y · 2013
Earlier work this paper cites.
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S. and Ben-David, S · 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.
Learning and inference in the presence of corrupted inputs
Feige, U., Mansour, Y., and Schapire, R · 2015
Earlier work this paper cites.
Supervised learning through the lens of compression
David, O., Moran, S., and Yehudayoff, A · 2016
Earlier work this paper cites.
Sample compression schemes for vc classes
Moran, S. and Yehudayoff, A · 2016
Earlier work this paper cites.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J., and Hsieh, C.-J · 2017
Earlier work this paper cites.
A general characterization of the statistical query complexity
Feldman, V · 2017
Earlier work this paper cites.
Simple black-box adversarial attacks on deep neural networks
Narodytska, N. and Kasiviswanathan, S · 2017
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Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Akhtar, N. and Mian, A · 2018
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2018
Cited alongside, same era.
Adversarial attacks and defences: A survey
Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., and Mukhopadhyay, D · 2018
Cited alongside, same era.
Pac-learning in the presence of adversaries
Improved generalization bounds for robust learning
Attias, I., Kontorovich, A., and Mansour, Y · 2019
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On robustness to adversarial examples and polynomial optimization
Awasthi, P., Dutta, A., and Vijayaraghavan, A · 2019
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Adversarial examples from computational constraints
Bubeck, S., Lee, Y. T., Price, E., and Razenshteyn, I. P · 2019
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Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J. C., and Liang, P · 2019
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Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
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Lower bounds for adversarially robust PAC learning
Diochnos, D. I., Mahloujifar, S., and Mahmoody, M · 2019
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Cullina, D., Bhagoji, A. N., and Mittal, P · 2018
Cited alongside, same era.
Adversarial risk and robustness: General definitions and implications for the uniform distribution
Diochnos, D., Mahloujifar, S., and Mahmoody, M · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., and Li, J · 2018
Cited alongside, same era.
Making machine learning robust against adversarial inputs
Goodfellow, I. J., McDaniel, P. D., and Papernot, N · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J. C · 2018
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Adversarially robust learning could leverage computational hardness
Garg, S., Jha, S., Mahloujifar, S., and Mahmoody, M · 2019
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When can unlabeled data improve the learning rate?
Göpfert, C., Ben-David, S., Bousquet, O., Gelly, S., Tolstikhin, I. O., and Urner, R · 2019
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On the hardness of robust classification
Gourdeau, P., Kanade, V., Kwiatkowska, M., and Worrell, J · 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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Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Li, J., Razenshteyn, I. P., Zhang, P., Zhang, H., Bubeck, S., and Yang, G · 2019
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One pixel attack for fooling deep neural networks
Su, J., Vargas, D. V., and Sakurai, K · 2019
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Adversarial examples for non-parametric methods: Attacks, defenses and large sample limits
Yang, Y., Rashtchian, C., Wang, Y., and Chaudhuri, K · 2019
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Rademacher complexity for adversarially robust generalization
Yin, D., Ramchandran, K., and Bartlett, P. L · 2019
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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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