14 citations · 22 across the 7 of their papers we have counts for
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stat.ML2026
Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy
Puning Zhao, Zhikun Zhang, Shaowei Wang +5
Local differential privacy (LDP) protocols are vulnerable to poisoning attacks. Existing research have proposed efficient defense strategies for single-item users. However, in prac…
cs.LG2026
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial attacks. However, FAT is prone…
cs.LG2026
Mitigating Error Amplification in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Bo Wang +3
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations. However, FAT often suffe…