84 citations · 375 across the 68 of their papers we have counts for
5 papers · 1 filter
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
Ruize Gao, Feng Liu, Jingfeng Zhang +4
The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of ad…
Geometry-aware Instance-reweighted Adversarial Training
Jingfeng Zhang, Jianing Zhu, Gang Niu +3
In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robus…
Robust Federated Recommendation System
Chen Chen, Jingfeng Zhang, Anthony K. H. Tung +2
Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning at…
Hierarchically Fair Federated Learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly +1
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the…
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han +4
Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. However, it is conservative or even pessimistic so that i…