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Adversarially robust machine learning has received much recent attention.
On Evaluating Adversarial Robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I. J., Madry, A., and Kurakin, A. (2019) · 1902
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Defense against adversarial images using web-scale nearest-neighbor search
Dubey, A., van der Maaten, L., Yalniz, Z., Li, Y., and Mahajan, D. (2019) · 1903
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Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Jordan, M., Lewis, J., and Dimakis, A. G. (2019) · 1903
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On the Robustness of Deep K-Nearest Neighbors
Sitawarin, C. and Wagner, D. (2019) · 1903
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Provably robust boosted decision stumps and trees against adversarial attacks
Andriushchenko, M. and Hein, M. (2019) · 1906
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Nearest neighbor pattern classification
Cover, T. M. and Hart, P. E. (1967) · 1967
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The Condensed Nearest Neighbor Rule
Hart, P. (1968) · 1968
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The Reduced Nearest Neighbor Rule
Gates, G. (1972) · 1972
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An nˆ5/2 algorithm for maximum matchings in bipartite graphs
Hopcroft, J. E. and Karp, R. M. (1973) · 1973
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Voronoi diagrams—a survey of a fundamental geometric data structure
Aurenhammer, F. (1991) · 1991
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On levels in arrangements and voronoi diagrams
Mulmuley, K. (1991) · 1991
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On the strong universal consistency of nearest neighbor regression function estimates
Devroye, L., Gyorfi, L., Krzyzak, A., and Lugosi, G. (1994) · 1994
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Random forests
Breiman, L. (2001) · 2001
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Convex Optimization
Boyd, S. and Vandenberghe, L. (2004) · 2004
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Adversarial learning
Lowd, D. and Meek, C. (2005) · 2005
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Introduction to Information Retrieval
Manning, C., Raghavan, P., and Schütze, H. (2010) · 2010
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Cited alongside, same era.
Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F. (2013) · 2013
Cited alongside, same era.
Rates of convergence for nearest neighbor classification
Chaudhuri, K. and Dasgupta, S. (2014) · 2014
Cited alongside, same era.
Linear-time approximation for maximum weight matching
Duan, R. and Pettie, S. (2014) · 2014
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
Cited alongside, same era.
A Bayes Consistent 1-NN classifier
Kontorovich, A. and Weiss, R. (2015) · 2015
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Athalye, A., Carlini, N., and Wagner, D. (2018) · 2018
Later among the works it cites.
Evaluation and Design of Robust Neural Network Defenses
Carlini, N. (2018) · 2018
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Gurobi optimizer reference manual
Gurobi Optimization, L. (2018) · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2018) · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Papernot, N. and McDaniel, P. (2018) · 2018
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Certified defenses against adversarial examples
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Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A. (2015) · 2015
Cited alongside, same era.
Evasion and Hardening of Tree Ensemble Classifiers
Kantchelian, A., Tygar, J., and Joseph, A. (2016) · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I. J., and Bengio, S. (2016) · 2016
Cited alongside, same era.
The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality
Amsaleg, L., Bailey, J., Barbe, D., Erfani, S., Houle, M. E., Nguyen, V., and Radovanović, M. (2017) · 2017
Cited alongside, same era.
Classification and regression trees
Breiman, L. (2017) · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. (2017) · 2017
Cited alongside, same era.
Raghunathan, A., Steinhardt, J., and Liang, P. (2018) · 2018
Later among the works it cites.
Certifiable Distributional Robustness with Principled Adversarial Training
Sinha, A., Namkoong, H., and Duchi, J. (2018) · 2018
Later among the works it cites.
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Wang, Y., Jha, S., and Chaudhuri, K. (2018) · 2018
Later among the works it cites.
Robust Decision Trees Against Adversarial Examples
Chen, H., Zhang, H., Boning, D., and Hsieh, C.-J. (2019) · 2019
Closest in time.
Query-efficient Hard-label Black-box Attack: An Optimization-based Approach
Cheng, M., Le, T., Chen, P.-Y., Yi, J., Zhang, H., and Hsieh, C.-J. (2019) · 2019
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Provable robustness of relu networks via maximization of linear regions
Croce, F., Andriushchenko, M., and Hein, M. (2019) · 2019
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Adversarial Training with Voronoi Constraints
Khoury, M. and Hadfield-Menell, D. (2019) · 2019
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Improving the Generalization of Adversarial Training with Domain Adaptation
Song, C., He, K., Wang, L., and Hopcroft, J. E. (2019) · 2019
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Evaluating Robustness of Neural Networks with Mixed Integer Programming
Tjeng, V., Xiao, K., and Tedrake, R. (2019) · 2019
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Training for faster adversarial robustness verification via inducing relu stability
Xiao, K. Y., Tjeng, V., Shafiullah, N. M., and Madry, A. (2019) · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., and Jordan, M. (2019) · 2019
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