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Adversarial training is a technique for training robust machine learning models.
On convergence proofs for perceptrons
Albert B Novikoff · 1962
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Dimitri P Bertsekas · 1971
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Bounds for unrestricted codes, by linear programming
Philippe Delsarte · 1972
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Bounds for packings on a sphere and in space
GA Kabatyanskiı and VI Levenshteın · 1974
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Tables of sphere packings and spherical codes
N Sloane · 1981
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Spherical codes and designs
Philippe Delsarte, Jean-Marie Goethals, and Johan Jacob Seidel · 1991
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Nonlinear programming
Dimitri P Bertsekas · 1997
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Boosting as a regularized path to a maximum margin classifier
Saharon Rosset, Ji Zhu, and Trevor Hastie · 2004
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Robustness and regularization of support vector machines
Huan Xu, Constantine Caramanis, and Shie Mannor · 2009
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Making gradient descent optimal for strongly convex stochastic optimization
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2011
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Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
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A primal-dual convergence analysis of boosting
Matus Telgarsky · 2012
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Robust optimization in machine learning
Constantine Caramanis, Shie Mannor, and Huan Xu · 2012
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The theory of max-min and its application to weapons allocation problems
John M Danskin · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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The rate of convergence of adaboost
Indraneel Mukherjee, Cynthia Rudin, and Robert E Schapire · 2013
Cited alongside, same era.
Margins, shrinkage, and boosting
Matus Telgarsky · 2013
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Sphere packings, lattices and groups
John Horton Conway and Neil James Alexander Sloane · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Sphere packing bounds via spherical codes
Henry Cohn, Yufei Zhao, et al · 2014
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
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Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2018
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Characterizing implicit bias in terms of optimization geometry
Suriya Gunasekar, Jason Lee, Daniel Soudry, and Nathan Srebro · 2018
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Implicit bias of gradient descent on linear convolutional networks
Suriya Gunasekar, Jason D Lee, Daniel Soudry, and Nati Srebro · 2018
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Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Cited alongside, same era.
Universal adversarial perturbations against semantic image segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer · 2017
Cited alongside, same era.
Fast feature fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri, Utsav Garg, and R Venkatesh Babu · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
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Risk and parameter convergence of logistic regression
Ziwei Ji and Matus Telgarsky · 2018
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Stochastic gradient descent on separable data: Exact convergence with a fixed learning rate
Mor Shpigel Nacson, Nathan Srebro, and Daniel Soudry · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Adversarial examples from computational constraints
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn · 2018
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Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 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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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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There is no free lunch in adversarial robustness (but there are unexpected benefits)
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Universal adversarial training
Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S Davis, and Tom Goldstein · 2018
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Adversarial examples are a natural consequence of test error in noise
Nic Ford, Justin Gilmer, Nicolas Carlini, and Dogus Cubuk · 2019
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Convergence of gradient descent on separable data
Mor Shpigel Nacson, Jason Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, and Daniel Soudry · 2019
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Does data augmentation lead to positive margin?
Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, and Dimitris Papailiopoulos · 2019
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