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Adversarial robustness is an increasingly critical property of classifiers in applications.
Statistical behavior and consistency of classification methods based on convex risk minimization
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Convex Optimization
Stephen P. Boyd and Lieven Vandenberghe · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Sequence to sequence learning with neural networks
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Learning and inference in the presence of corrupted inputs
Uriel Feige, Yishay Mansour, and Robert Schapire · 2015
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Nicholas Carlini and David Wagner · 2017
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Idan Attias, Aryeh Kontorovich, and Yishay Mansour · 2018
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PAC-learning in the presence of evasion adversaries
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal · 2018
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Robust inference for multiclass classification
Uriel Feige, Yishay Mansour, and Robert E Schapire · 2018
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Vc classes are adversarially robustly learnable, but only improperly
Omar Montasser, Steve Hanneke, and Nathan Srebro · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Rademacher complexity for adversarially robust generalization
Dong Yin, Kannan Ramchandran, and Peter L. Bartlett · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Adversarial learning guarantees for linear hypotheses and neural networks
Pranjal Awasthi, Natalie Frank, and Mehryar Mohri · 2020
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Robustness may be at odds with accuracy
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On robustness to adversarial examples and polynomial optimization
Pranjal Awasthi, Abhratanu Dutta, and Aravindan Vijayaraghavan · 2019
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
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Adversarial examples from cryptographic pseudo-random generators
Sébastien Bubeck, Yin Tat Lee, Eric Price, and Ilya Razenshteyn
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Adversarial examples from computational constraints
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn
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Calibrated surrogate losses for adversarially robust classification
Han Bao, Clayton Scott, and Masashi Sugiyama · 2020
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The complexity of adversarially robust proper learning of halfspaces with agnostic noise
Ilias Diakonikolas, Daniel M Kane, and Pasin Manurangsi · 2020
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Reducing adversarially robust learning to non-robust pac learning
Omar Montasser, Steve Hanneke, and Nathan Srebro · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
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Bayes consistency vs. h-consistency: The interplay between surrogate loss functions and the scoring function class
Mingyuan Zhang and Shivani Agarwal · 2020
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