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Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited.
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Friedman, J. H · 2001
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Friedman, J. H · 2002
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LIBSVM: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
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Parallel boosted regression trees for web search ranking
Tyree, S., Weinberger, K. Q., Agrawal, K., and Paykin, J · 2011
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Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
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XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Evasion and hardening of tree ensemble classifiers
Kantchelian, A., Tygar, J., and Joseph, A · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J., and Hsieh, C.-J · 2017
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Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L · 2017
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Adversarial attacks on neural network policies
Huang, S., Papernot, N., Goodfellow, I., Duan, Y., and Abbeel, P · 2017
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Query-efficient black-box adversarial examples
Ilyas, A., Engstrom, L., Athalye, A., and Lin, J · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
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Cost efficient gradient boosting
Peter, S., Diego, F., Hamprecht, F. A., and Nadler, B · 2017
Cited alongside, same era.
Gradient boosted decision trees for high dimensional sparse output
Si, S., Zhang, H., Keerthi, S. S., Mahajan, D., Dhillon, I. S., and Hsieh, C.-J · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
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Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
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Mixtrain: Scalable training of formally robust neural networks
Wang, S., Chen, Y., Abdou, A., and Jana, S · 2018
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Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y · 2017
Cited alongside, same era.
Deep forest: Towards an alternative to deep neural networks
Zhou, Z.-H. and Feng, J · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Thermometer encoding: One hot way to resist adversarial examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
Cited alongside, same era.
Audio adversarial examples: Targeted attacks on speech-to-text
Carlini, N. and Wagner, D · 2018
Cited alongside, same era.
Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Cheng, M., Yi, J., Zhang, H., Chen, P.-Y., and Hsieh, C.-J · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, J. Z · 2018
Later among the works it cites.
Scaling provable adversarial defenses
Wong, E., Schmidt, F., Metzen, J. H., and Kolter, J. Z · 2018
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GPU-acceleration for large-scale tree boosting
Zhang, H., Si, S., and Hsieh, C.-J · 2018
Later among the works it cites.
Query-efficient hard-label black-box attack: An optimization-based approach
Cheng, M., Le, T., Chen, P.-Y., Zhang, H., Yi, J., and Hsieh, C.-J · 2019
Closest in time.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Rob-gan: Generator, discriminator, and adversarial attacker
Liu, X. and Hsieh, C.-J · 2019
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Adv-bnn: Improved adversarial defense through robust bayesian neural network
Liu, X., Li, Y., Wu, C., and Hsieh, C.-J · 2019
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
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Gradient regularized budgeted boosting
Xu, Z. E., Kusner, M. J., Weinberger, K. Q., and Zheng, A. X · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2019
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