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Modern machine learning algorithms perform poorly on adversarially manipulated data.
A robust version of the probability ratio test
P. J. Huber · 1965
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Topics in Optimal Transportation
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Robust Statistics
P. J. Huber · 2004
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D. Leao Jr, M. Fragoso, and P. Ruffino · 2004
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J. Goh and M. Sim · 2010
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A-R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury · 2012
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Convergence of Probability Measures
P. Billingsley · 2013
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Speech recognition with deep recurrent neural networks
A. Graves, A-R. Mohamed, and G. Hinton · 2013
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Robust hypothesis testing for modeling errors
G. Gül and A. M. Zoubir · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Distributionally robust convex optimization
W. Wiesemann, D. Kuhn, and M. Sim · 2014
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Robustifying convex risk measures for linear portfolios: A nonparametric approach
D. Wozabal · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Multi-marginal optimal transport: theory and applications
B. Pass · 2015
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Optimal transport for applied mathematicians
F. Santambrogio · 2015
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Distributionally robust logistic regression
S. Shafieezadeh Abadeh, M. Esfahani, Peyman. M., and d. Kuhn · 2015
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Distributionally robust stochastic optimization with Wasserstein distance
R. Gao and A. J. Kleywegt · 2016
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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A new method for estimation and model selection: ρ \rho -estimation
Y. Baraud, L. Birgé, and M. Sart · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
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Recent trends in deep learning based natural language processing [review article]
T. Young, D. Hazarika, S. Poria, and E. Cambria · 2018
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Efficient neural network robustness certification with general activation functions
H. Zhang, T-W. Weng, P-Y. Chen, C-J. Hsieh, and L. Daniel · 2018
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Are adversarial examples inevitable?
Shafahi A., W. R. Huang, S. Studer, S. Feizi, and T. Goldstein · 2019
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Lower bounds on adversarial robustness from optimal transport
A. N. Bhagoji, D. Cullina, and P. Mittal · 2019
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Robust Wasserstein profile inference and applications to machine learning
J. Blanchet, Y. Kang, and K. Murthy · 2019
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Quantifying distributional model risk via optimal transport
J. Blanchet and K. Murthy · 2019
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M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
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Wasserstein distributional robustness and regularization in statistical learning
R. Gao, X. Chen, and A. J. Kleywegt · 2017
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Minimax robust hypothesis testing
G. Gül and A. M. Zoubir · 2017
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2017
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Distributionally robust deep learning as a generalization of adversarial training
M. Staib and S. Jegelka · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and Wagner D. A · 2018
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Certified adversarial robustness via randomized smoothing
J.M. Cohen, E. Rosenfeld, and J. Z. Kolter · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M-W. Chang, K. Lee, and K. Toutanova · 2019
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Limitations of adversarial robustness: Strong no free lunch theorem
E. Dohmatob · 2019
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On the hardness of robust classification
P. Gourdeau, V. Kanade, M. Kwiatkowska, and J. Worrell · 2019
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Adversarial examples are not bugs, they are features
A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry · 2019
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The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure
S. Mahloujifar, D. I. Diochnos, and M. Mahmoody · 2019
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Robustness via curvature regularization, and vice versa
S-M. Moosavi-Dezfooli, A. Fawzi, J. Uesato, and P. Frossard · 2019
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Computational optimal transport
G. Peyré and M. Cuturi · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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Theoretical analysis of adversarial learning: A minimax approach
Z. Tu, J. Zhang, and D. Tao · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, J. Oh, D. Horgan, M. Kroiss, I. Danihelka, A. Huang, L. Sifre, T. Cai, J. P. Agapiou, M. Jaderberg, A. S. Vezhnevets, R. Leblond, T. Pohlen, V. Dalibard, D. Budden, Y. Sulsky, J. Molloy, T. L. Paine, C. Gulcehre, Z. Wang, T. Pfaff, R. Ring, D. Yogatama, D. Wünsch, K. McKinney, O. Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, and D. Silver · 2019
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Rademacher complexity for adversarially robust generalization
D. Yin, K. Ramchandran, and P. Bartlett · 2019
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Generalized resilience and robust statistics
B. Zhu, J. Jiao, and J. Steinhardt · 2019
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Calibrated surrogate losses for adversarially robust classification
H. Bao, C. Scott, and M. Sugiyama · 2020
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A data-driven approach to multi-stage stochastic linear optimization
D. Bertsimas, S. Shtern, and B. Sturt · 2020
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When are non-parametric methods robust?
R. Bhattacharjee and K. Chaudhuri · 2020
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Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications
V. Jog · 2020
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Minimax classification with 0-1 loss and performance guarantees
S. Mazuelas, A. Zanoni, and A. Pérez · 2020
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Randomization matters. how to defend against strong adversarial attacks
R. Pinot, R. Ettedgui, G. Rizk, Y. Chevaleyre, and J. Atif · 2020
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Robustness for non-parametric classification: A generic attack and defense
Y-Y. Yang, C. Rashtchian, Y. Wang, and K. Chaudhuri · 2020
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A closer look at accuracy vs. robustness
Y-Y. Yang, C. Rashtchian, H. Zhang, R. R. Salakhutdinov, and K. Chaudhuri · 2020
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