Prati
Han Xu
Han Xu
Potvrđena adresa e-pošte na msu.edu - Početna stranica
Naslov
Citirano
Citirano
Godina
Adversarial attacks and defenses in images, graphs and text: A review
H Xu, Y Ma, H Liu, D Deb, H Liu, J Tang, A Jain, K
International Journal of Automation and Computing 17 (2), 151-178, 2020
3822020
Adversarial attacks and defenses on graphs
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang
ACM SIGKDD Explorations Newsletter 22 (2), 19-34, 2021
1372021
Deeprobust: A pytorch library for adversarial attacks and defenses
Y Li, W Jin, H Xu, J Tang
arXiv preprint arXiv:2005.06149, 2020
752020
To be robust or to be fair: Towards fairness in adversarial training
H Xu, X Liu, Y Li, A Jain, J Tang
International Conference on Machine Learning, 11492-11501, 2021
572021
Graph neural networks with adaptive residual
X Liu, J Ding, W Jin, H Xu, Y Ma, Z Liu, J Tang
Advances in Neural Information Processing Systems 34, 9720-9733, 2021
122021
Covariance-insured screening
K He, J Kang, HG Hong, J Zhu, Y Li, H Lin, H Xu, Y Li
Computational statistics & data analysis 132, 100-114, 2019
102019
Jointly attacking graph neural network and its explanations
W Fan, W Jin, X Liu, H Xu, X Tang, S Wang, Q Li, J Tang, J Wang, ...
arXiv preprint arXiv:2108.03388, 2021
92021
Deeprobust: a platform for adversarial attacks and defenses
Y Li, W Jin, H Xu, J Tang
Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16078 …, 2021
92021
Deep adversarial canonical correlation analysis
W Fan, Y Ma, H Xu, X Liu, J Wang, Q Li, J Tang
Proceedings of the 2020 SIAM International Conference on Data Mining, 352-360, 2020
82020
Adversarial attacks and defenses: Frontiers, advances and practice
H Xu, Y Li, W Jin, J Tang
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
72020
A selective overview of feature screening methods with applications to neuroimaging data
K He, H Xu, J Kang
Wiley Interdisciplinary Reviews: Computational Statistics 11 (2), e1454, 2019
62019
Imbalanced adversarial training with reweighting
W Wang, H Xu, X Liu, Y Li, B Thuraisingham, J Tang
arXiv preprint arXiv:2107.13639, 2021
42021
Yet meta learning can adapt fast, it can also break easily
H Xu, Y Li, X Liu, H Liu, J Tang
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
42021
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang
arXiv preprint arXiv:2003.00653, 2003
32003
Transferable Unlearnable Examples
J Ren, H Xu, Y Wan, X Ma, L Sun, J Tang
arXiv preprint arXiv:2210.10114, 2022
2022
Towards Fair Classification against Poisoning Attacks
H Xu, X Liu, Y Wan, J Tang
arXiv preprint arXiv:2210.09503, 2022
2022
Probabilistic Categorical Adversarial Attack & Adversarial Training
P He, H Xu, J Ren, Y Wan, Z Liu, J Tang
arXiv preprint arXiv:2210.09364, 2022
2022
Towards Generating Adversarial Examples on Mixed-type Data
H Xu, M Pan, Z Jiang, H Chen, X Li, M Das, H Yang
arXiv preprint arXiv:2210.09405, 2022
2022
A Comprehensive Survey on Trustworthy Recommender Systems
W Fan, X Zhao, X Chen, J Su, J Gao, L Wang, Q Liu, Y Wang, H Xu, ...
arXiv preprint arXiv:2209.10117, 2022
2022
Towards Adversarial Learning: From Evasion Attacks to Poisoning Attacks
W Wang, H Xu, Y Wan, J Ren, J Tang
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
2022
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