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Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.
The use of confidence or fiducial limits illustrated in the case of the binomial
Clopper, C. J. and Pearson, E. S · 1934
Earlier work this paper cites.
Extension of the neyman-pearson theory of tests to discontinuous variates
Tocher, K. D · 1950
Earlier work this paper cites.
Statistical applications of the poisson-binomial and conditional bernoulli distributions
Chen, S. X. and Liu, J. S · 1997
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
Closed-form expression for the poisson-binomial probability density function
Fernández, M. and Williams, S · 2010
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Earlier work this paper cites.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I. J., and Bengio, S · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D. A · 2018
Cited alongside, same era.
Adversarial attack on graph structured data
Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L · 2018
Cited alongside, same era.
Second-order adversarial attack and certifiable robustness
Li, B., Chen, C., Wang, W., and Carin, L · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Semidefinite relaxations for certifying robustness to adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
Cited alongside, same era.
Tight certificates of adversarial robustness for randomly smoothed classifiers
Lee, G., Yuan, Y., Chang, S., and Jaakkola, T. S · 2019
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Robustness certificates for sparse adversarial attacks by randomized ablation
Levine, A. and Feizi, S · 2019
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Fake news detection on social media using geometric deep learning
Monti, F., Frasca, F., Eynard, D., Mannion, D., and Bronstein, M. M · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Li, J., Razenshteyn, I. P., Zhang, P., Zhang, H., Bubeck, S., and Yang, G · 2019
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Fdgars: Fraudster detection via graph convolutional networks in online app review system
Wang, J., Wen, R., Wu, C., Huang, Y., and Xion, J · 2019
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Rhee, S., Seo, S., and Kim, S · 2018
Cited alongside, same era.
Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, J. Z · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data
Zügner, D., Akbarnejad, A., and Günnemann, S · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J. C., and Liang, P · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2019
Cited alongside, same era.
Topology attack and defense for graph neural networks: An optimization perspective
Xu, K., Chen, H., Liu, S., Chen, P., Weng, T., Hong, M., and Lin, X · 2019
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A framework for robustness certification of smoothed classifiers using f-divergences
Dvijotham, K. D., Hayes, J., Balle, B., Kolter, Z., Qin, C., Gyorgy, A., Xiao, K., Gowal, S., and Kohli, P · 2020
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All you need is low (rank): Defending against adversarial attacks on graphs
Entezari, N., Al-Sayouri, S. A., Darvishzadeh, A., and Papalexakis, E. E · 2020
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Certified robustness of community detection against adversarial structural perturbation via randomized smoothing
Jia, J., Wang, B., Cao, X., and Gong, N. Z · 2020
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2020
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Hierarchical propagation networks for fake news detection: Investigation and exploitation
Shu, K., Mahudeswaran, D., Wang, S., and Liu, H · 2020
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Certifiable robustness of graph convolutional networks under structure perturbations
Zügner, D. and Günnemann, S · 2020
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