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Recent research shows the susceptibility of machine learning models to adversarial attacks, wherein minor but maliciously chosen perturbations of the input can significantly degrade model performance.
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Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
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Yin, D., Ramchandran, K., and Bartlett, P. L. (2019) · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I. (2019) · 2019
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Adversarial learning guarantees for linear hypotheses and neural networks
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When are non-parametric methods robust?
Bhattacharjee, R. and Chaudhuri, K. (2020) · 2020
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Sharp statistical guaratees for adversarially robust Gaussian classification
Dan, C., Wei, Y., and Ravikumar, P. (2020) · 2020
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A survey of deep learning techniques for autonomous driving
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Non-asymptotic bounds for adversarial excess risk under misspecified models
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Overparameterized linear regression under adversarial attacks
Ribeiro, A. H. and Schön, T. B. (2023) · 2023
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Binary classification under ℓ 0 \ell_{0} attacks for general noise distribution
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Local convergence rates of the nonparametric least squares estimator with applications to transfer learning
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