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Why are classifiers in high dimension vulnerable to "adversarial" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints.
Orthogonal polynomials , volume 23
Gabor Szego · 1939
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Weakly learning dnf and characterizing statistical query learning using fourier analysis
Avrim Blum, Merrick Furst, Jeffrey Jackson, Michael Kearns, Yishay Mansour, and Steven Rudich · 1994
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Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Adversarial classification
Nilesh Dalvi, Pedro Domingos, Mausam, Sumit Sanghai, and Deepak Verma · 2004
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Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2004
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Nightmare at test time: Robust learning by feature deletion
Amir Globerson and Sam Roweis · 2006
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Unconditional lower bounds for learning intersections of halfspaces
Adam R. Klivans and Alexander A. Sherstov · 2007
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Statistical algorithms and a lower bound for detecting planted cliques
Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh Vempala, and Ying Xiao · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Ilias Diakonikolas, Daniel Kane, and Alistair Stewart · 2017
Cited alongside, same era.
A general characterization of the statistical query complexity
Vitaly Feldman · 2017
Cited alongside, same era.
On the complexity of learning neural networks
Le Song, Santosh Vempala, John Wilmes, and Bo Xie · 2017
Cited alongside, same era.
Adversarial examples that fool both human and computer vision
Gamaleldin F Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
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Adversarial vulnerability for any classifier, 2018
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
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Asymptotic approximations to the nodes and weights of gauss–hermite and gauss–laguerre quadratures
Amparo Gil, Javier Segura, and Nico M Temme · 2018
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Justin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
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Towards deep learning models resistant to adversarial attacks
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Yizhen Wang, Somesh Jha, and Kamalika Chaudhuri · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Adversarially robust generalization requires more data, 2018
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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