3 papers
cs.LG2026
PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification
Wumei Du, Jiarong Wen, Kaiyu Zhang +5
Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as misclassification of malici…
cs.LG2024
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning
Yiqin Lv, Qi Wang, Dong Liang +1
Meta learning is a promising paradigm in the era of large models and task distributional robustness has become an indispensable consideration in real-world scenarios. Recent advanc…
stat.ML2024
Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
Wumei Du, Dong Liang, Yiqin Lv +4
Internet services have led to the eruption of network traffic, and machine learning on these Internet data has become an indispensable tool, especially when the application is risk…