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It has been consistently reported that many machine learning models are susceptible to adversarial attacks i.e., small additive adversarial perturbations applied to data points can cause misclassification.
Some inequalities for gaussian processes and applications
Yehoram Gordon · 1985
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R Tyrrell Rockafellar and Roger J-B Wets · 2009
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Various thresholds for ℓ 1 \ell_{1} -optimization in compressed sensing
Mihailo Stojnic · 2009
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Optimal m-estimation in high-dimensional regression
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A framework to characterize performance of lasso algorithms
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Intriguing properties of neural networks. arxiv 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Universality in polytope phase transitions and message passing algorithms
Mohsen Bayati, Marc Lelarge, Andrea Montanari, et al · 2015
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Regularized linear regression: A precise analysis of the estimation error
Christos Thrampoulidis, Samet Oymak, and Babak Hassibi · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2018
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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Emmanuel J Candès and Pragya Sur · 2018
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Universality laws for randomized dimension reduction, with applications
Samet Oymak and Joel A Tropp · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Universality in learning from linear measurements
Ehsan Abbasi, Fariborz Salehi, and Babak Hassibi · 2019
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Lower bounds on adversarial robustness from optimal transport
Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2019
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Mikhail Belkin, Daniel Hsu, and Ji Xu · 2019
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A modern maximum-likelihood theory for high-dimensional logistic regression
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Theoretically principled trade-off between robustness and accuracy
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A precise performance analysis of learning with random features
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Unlabeled data improves adversarial robustness
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Convergence and margin of adversarial training on separable data
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A model of double descent for high-dimensional binary linear classification
Zeyu Deng, Abla Kammoun, and Christos Thrampoulidis · 2019
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Limitations of lazy training of two-layers neural network
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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Xiaoyi Mai and Zhenyu Liao · 2019
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A large scale analysis of logistic regression: Asymptotic performance and new insights
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Oussama Dhifallah and Yue M Lu · 2020
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Sharp statistical guarantees for adversarially robust gaussian classification
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Sharp asymptotics and optimal performance for inference in binary models
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