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Adversarial or test time robustness measures the susceptibility of a classifier to perturbations to the test input.
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Polyakova, L · 1984
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Probability in Banach Spaces: Isoperimetry and Processes
Ledoux, M. and Talagrand, M · 1991
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Learning in the presence of malicious errors
Kearns, M. and Li, M · 1993
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Toward efficient agnostic learning
Kearns, M. J., Schapire, R. E., and Sellie, L. M · 1994
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On some inequalities for the Gamma and Psi functions
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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Kakade, S. M., Sridharan, K., and Tewari, A · 2008
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The NIST Handbook of Mathematical Functions
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Robust statistics
Huber, P. J · 2011
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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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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Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
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AdaNet: Adaptive structural learning of artificial neural networks
Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M., and Yang, S · 2017
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Towards deep learning models resistant to adversarial attacks
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 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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Towards the first adversarially robust neural network model on mnist
Schott, L., Rauber, J., Bethge, M., and Brendel, W · 2018
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Robustness may be at odds with accuracy, 2018
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
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On robustness to adversarial examples and polynomial optimization
Awasthi, P., Dutta, A., and Vijayaraghavan, A · 2019
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Breaking the madry defense model with l _ 1 l\_1 -based adversarial examples
Sharma, Y. and Chen, P.-Y · 2017
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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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Audio adversarial examples: Targeted attacks on speech-to-text
Carlini, N. and Wagner, D · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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On the effectiveness of interval bound propagation for training verifiably robust models
Gowal, S., Dvijotham, K., Stanforth, R., Bunel, R., Qin, C., Uesato, J., Arandjelovic, R., Mann, T., and Kohli, P · 2018
Cited alongside, same era.
Adversarial risk bounds via function transformation
Khim, J. and Loh, P.-L · 2018
Cited alongside, same era.
Degwekar, A., Nakkiran, P., and Vaikuntanathan, V · 2019
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An alternative surrogate loss for pgd-based adversarial testing
Gowal, S., Uesato, J., Qin, C., Huang, P.-S., Mann, T., and Kohli, 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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Adversarial robustness may be at odds with simplicity
Nakkiran, P · 2019
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Adversarial training can hurt generalization
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J. C., and Liang, P · 2019
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Rademacher complexity for adversarially robust generalization
Yin, D., Ramchandran, K., and Bartlett, P. L · 2019
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