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Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances.
Problèmes concrets d’analyse fonctionnelle
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Christer Borell · 1975
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Vladimir N Sudakov and Boris S Tsirel’son · 1978
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Unconditional and symmetric sets inn-dimensional normed spaces
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λ \lambda 1, isoperimetric inequalities for graphs, and superconcentrators
Noga Alon and Vitali D Milman · 1985
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Asymptotic theory of finite dimensional normed spaces
Vitali D Milman and Gideon Schechtman · 1986
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Michael J. Kearns and Ming Li · 1993
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Concentration of measure and isoperimetric inequalities in product spaces
Michel Talagrand · 1995
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Euclidean structure in finite dimensional normed spaces
Apostolos A Giannopoulos and Vitali D Milman · 2001
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The Concentration of Measure Phenomenon
Michel Ledoux · 2001
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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PAC learning with nasty noise
Nader H. Bshouty, Nadav Eiron, and Eyal Kushilevitz · 2002
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Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Antidote: understanding and defending against poisoning of anomaly detectors
Benjamin I.P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J.D. Tygar · 2009
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Security evaluation of pattern classifiers under attack
Battista Biggio, Giorgio Fumera, and Fabio Roli · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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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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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Robust probabilistic inference
Yishay Mansour, Aviad Rubinstein, and Moshe Tennenholtz · 2015
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Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli · 2015
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Improved generalization bounds for robust learning
Idan Attias, Aryeh Kontorovich, and Yishay Mansour · 2018
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Adversarial examples from computational constraints
Sébastien Bubeck, Eric Price, and Ilya Razenshteyn · 2018
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Robustly learning a gaussian: Getting optimal error, efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2018
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2018
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List-decodable robust mean estimation and learning mixtures of spherical Gaussians
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2018
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
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Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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A uror: defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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The Power of Localization for Efficiently Learning Linear Separators with Noise
Pranjal Awasthi, Maria-Florina Balcan, and Philip M. Long · 2017
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Efficient algorithms and lower bounds for robust linear regression
Ilias Diakonikolas, Weihao Kong, and Alistair Stewart · 2018
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Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
Dimitrios I. Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2018
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Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
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Robust inference for multiclass classification
Uriel Feige, Yishay Mansour, and Robert E Schapire · 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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Making machine learning robust against adversarial inputs
Ian J. Goodfellow, Patrick D. McDaniel, and Nicolas Papernot · 2018
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Learning under p p -Tampering Attacks
Saeed Mahloujifar, Dimitrios I Diochnos, and Mohammad Mahmoody · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Robust estimation via robust gradient estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, and Pradeep Ravikumar · 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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Data Poisoning Attacks against Online Learning
Yizhen Wang and Kamalika Chaudhuri · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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