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Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing.
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Adversarial learning,
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Using cart to generate partially synthetic public use microdata,
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Feature weighting for improved classifier robustness,
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Distance metric learning for large margin nearest neighbor classification,
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Multiple classifier systems for robust classifier design in adversarial environments,
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Support vector machines under adversarial label noise,
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Adaptive subgradient methods for online learning and stochastic optimization.,
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Evasion attacks against machine learning at test time,
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, F. Roli, · 2013
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Intriguing properties of neural networks,
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Explaining and harnessing adversarial examples,
I. J. Goodfellow, J. Shlens, C. Szegedy, · 2014
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Generative adversarial nets,
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, · 2014
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Adam: A method for stochastic optimization,
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M. A. Nielsen, Neural networks and deep learning, volume 2018, Determination press San Francisco, CA, USA:, 2015
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,
M. Sharif, S. Bhagavatula, L. Bauer, M. K. Reiter, · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings,
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, A. Swami, · 2016
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Adversarial examples in the physical world,
A. Kurakin, I. Goodfellow, S. Bengio, · 2016
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Adversarial perturbations against deep neural networks for malware classification,
K. Grosse, N. Papernot, P. Manoharan, M. Backes, P. McDaniel, · 2016
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Robust classification of financial risk,
S. K. Sarkar, K. Oshiba, D. Giebisch, Y. Singer, · 2018
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Counterfactual explanations without opening the black box: automated decisions and the gdpr,
S. Wachter, B. Mittelstadt, C. Russell, · 2018
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One pixel attack for fooling deep neural networks,
J. Su, D. V. Vargas, K. Sakurai, · 2019
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Tabnet: Attentive interpretable tabular learning,
S. O. Arik, T. Pfister, · 2019
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Neural oblivious decision ensembles for deep learning on tabular data,
S. Popov, S. Morozov, A. Babenko, · 2019
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Provably robust boosted decision stumps and trees against adversarial attacks,
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Transferability in machine learning: From phenomena to black-box attacks using adversarial samples,
N. Papernot, P. McDaniel, I. Goodfellow, · 2016
Cited alongside, same era.
Evasion and hardening of tree ensemble classifiers,
A. Kantchelian, J. D. Tygar, A. Joseph, · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks,
N. Carlini, D. Wagner, · 2017
Cited alongside, same era.
Generating adversarial malware examples for black-box attacks based on gan,
W. Hu, Y. Tan, · 2017
Cited alongside, same era.
Privbayes: Private data release via bayesian networks,
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, X. Xiao, · 2017
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Lightgbm: A highly efficient gradient boosting decision tree,
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.-Y. Liu, · 2017
Cited alongside, same era.
In defense of the triplet loss for person re-identification,
A. Hermans, L. Beyer, B. Leibe, · 2017
Cited alongside, same era.
M. Andriushchenko, M. Hein, · 2019
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Robust decision trees against adversarial examples,
H. Chen, H. Zhang, D. Boning, C.-J. Hsieh, · 2019
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Query-efficient hard-label black-box attack: An optimization-based approach,
M. Cheng, T. Le, P.-Y. Chen, H. Zhang, J. Yi, C.-J. Hsieh, · 2019
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Imperceptible adversarial attacks on tabular data,
V. Ballet, X. Renard, J. Aigrain, T. Laugel, P. Frossard, M. Detyniecki, · 2019
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Actionable recourse in linear classification,
B. Ustun, A. Spangher, Y. Liu, · 2019
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Modeling tabular data using conditional gan,
L. Xu, M. Skoularidou, A. Cuesta-Infante, K. Veeramachaneni, · 2019
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Dace: Distribution-aware counterfactual explanation by mixed-integer linear optimization,
K. Kanamori, T. Takagi, K. Kobayashi, H. Arimura, · 2020
Closest in time.
H. C. Group, Home credit default risk, https://www.kaggle.com/c/home-credit-default-risk/data , 2018. Accessed August 2021
2021
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G. W. i. D. S. C. The Global Open Source Severity of Illness Score Consortium, Intensive care unit, https://www.kaggle.com/c/widsdatathon2020/data , 2018. Accessed August 2021
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I. Airbnb, Lodging airbnb listings in major U.S. cities, http://insideairbnb.com/get-the-data.html , 2018. Accessed August 2021
2021
Closest in time.
Adversarial examples for evaluating reading comprehension systems,
R. Jia, P. Liang, · 2031
Closest in time.