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Understanding the fundamental limits of robust supervised learning has emerged as a problem of immense interest, from both practical and theoretical standpoints.
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Towards the science of security and privacy in machine learning
Papernot, N., McDaniel, P., Sinha, A., and Wellman, M · 2016
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Improving the robustness of deep neural networks via stability training
Zheng, S., Song, Y., Leung, T., and Goodfellow, I · 2016
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Wild patterns: Ten years after the rise of adversarial machine learning
Biggio, B. and Roli, F · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Practical black-box attacks on deep neural networks using efficient query mechanisms
Bhagoji, A. N., He, W., Li, B., and Song, D · 2018
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Adversarial examples from computational constraints
Bubeck, S., Price, E., and Razenshteyn, I · 2018
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Pac-learning in the presence of adversaries
Cullina, D., Bhagoji, A. N., and Mittal, P · 2018
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Adversarial risk and robustness: General definitions and implications for the uniform distribution
Diochnos, D., Mahloujifar, S., and Mahmoody, M · 2018
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A survey on security threats and defensive techniques of machine learning: A data driven view
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., and Leung, V. C · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, 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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Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Sharp statistical guaratees for adversarially robust Gaussian classification
Dan, C., Wei, Y., and Ravikumar, P · 2020
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Security and machine learning in the real world
Evtimov, I., Cui, W., Kamar, E., Kiciman, E., Kohno, T., and Li, J · 2020
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Adversarially robust learning could leverage computational hardness
Garg, S., Jha, S., Mahloujifar, S., and Mohammad, M · 2020
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2020
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Awasthi, P., Dutta, A., and Vijayaraghavan, A · 2019
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Lower bounds on adversarial robustness from optimal transport
Bhagoji, A. N., Cullina, D., and Mittal, P · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., and Duchi, J. C · 2019
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Generalized no free lunch theorem for adversarial robustness
Dohmatob, E · 2019
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Adversarial robustness via label-smoothing
Goibert, M. and Dohmatob, E · 2019
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The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure
Mahloujifar, S., Diochnos, D. I., and Mahmoody, M · 2019
Cited alongside, same era.
Vc classes are adversarially robustly learnable, but only improperly
Montasser, O., Hanneke, S., and Srebro, N · 2019
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Precise tradeoffs in adversarial training for linear regression
Javanmard, A., Soltanolkotabi, M., and Hassani, H · 2020
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Sok: Certified robustness for deep neural networks
Li, L., Qi, X., Xie, T., and Li, B · 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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Adversarial risk via optimal transport and optimal couplings
Pydi, M. S. and Jog, V · 2020
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, Z · 2020
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Hydra: Pruning adversarially robust neural networks
Sehwag, V., Wang, S., Mittal, P., and Jana, S · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P. e. a · 2020
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Improving adversarial robustness requires revisiting misclassified examples
Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., and Gu, Q · 2020
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Adversarial weight perturbation helps robust generalization
Wu, D., Xia, S.-T., and Wang, Y · 2020
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Xie, C., Tan, M., Gong, B., Yuille, A., and Le, Q. V · 2020
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Bag of tricks for adversarial training
Pang, T., Yang, X., Dong, Y., Su, H., and Zhu, J · 2021
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